際際滷shows by User: RedmondRaminShamshir / http://www.slideshare.net/images/logo.gif 際際滷shows by User: RedmondRaminShamshir / Mon, 15 Mar 2021 23:51:20 GMT 際際滷Share feed for 際際滷shows by User: RedmondRaminShamshir Development and Field Evaluation of a Multichannel LoRa Sensor for IoT Monitoring in Berry Orchards /slideshow/development-and-field-evaluation-of-a-multichannel-lora-sensor-for-iot-monitoring-in-berry-orchards/244457225 shamshirigil2021lorasensors-210315235120
Evaluation of long-range wireless transceivers with respect to their power consumption, network connectivity, and coverage under extreme field conditions is necessary prior to their deployment in large scale commercial orchards. This paper reports on the development and field performance of an affordable multi-channel wireless data acquisition for IoT monitoring of environmental variations in berry orchards. A connectivity board was custom-designed based on the powerful dual-core 32-bit microcontroller with WiFi antenna and LoRa modulation at 868MHz. The objective was to verify the possibility of transmitting multiple sensor readings with lower power consumption while increasing the reliability and stability of wireless communication at long distances (over 1.7 km). Collected data from the wireless sensor was compared and found to be consistent with measurements of a data logger installed in the same locations. The presented paper highlights the advantages of LoRa sensors for digital agriculture and the experience in real-time monitoring of environmental parameters in berry orchards.]]>
Evaluation of long-range wireless transceivers with respect to their power consumption, network connectivity, and coverage under extreme field conditions is necessary prior to their deployment in large scale commercial orchards. This paper reports on the development and field performance of an affordable multi-channel wireless data acquisition for IoT monitoring of environmental variations in berry orchards. A connectivity board was custom-designed based on the powerful dual-core 32-bit microcontroller with WiFi antenna and LoRa modulation at 868MHz. The objective was to verify the possibility of transmitting multiple sensor readings with lower power consumption while increasing the reliability and stability of wireless communication at long distances (over 1.7 km). Collected data from the wireless sensor was compared and found to be consistent with measurements of a data logger installed in the same locations. The presented paper highlights the advantages of LoRa sensors for digital agriculture and the experience in real-time monitoring of environmental parameters in berry orchards.]]> Mon, 15 Mar 2021 23:51:20 GMT /slideshow/development-and-field-evaluation-of-a-multichannel-lora-sensor-for-iot-monitoring-in-berry-orchards/244457225 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Development and Field Evaluation of a Multichannel LoRa Sensor for IoT Monitoring in Berry Orchards RedmondRaminShamshir Evaluation of long-range wireless transceivers with respect to their power consumption, network connectivity, and coverage under extreme field conditions is necessary prior to their deployment in large scale commercial orchards. This paper reports on the development and field performance of an affordable multi-channel wireless data acquisition for IoT monitoring of environmental variations in berry orchards. A connectivity board was custom-designed based on the powerful dual-core 32-bit microcontroller with WiFi antenna and LoRa modulation at 868MHz. The objective was to verify the possibility of transmitting multiple sensor readings with lower power consumption while increasing the reliability and stability of wireless communication at long distances (over 1.7 km). Collected data from the wireless sensor was compared and found to be consistent with measurements of a data logger installed in the same locations. The presented paper highlights the advantages of LoRa sensors for digital agriculture and the experience in real-time monitoring of environmental parameters in berry orchards. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shamshirigil2021lorasensors-210315235120-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Evaluation of long-range wireless transceivers with respect to their power consumption, network connectivity, and coverage under extreme field conditions is necessary prior to their deployment in large scale commercial orchards. This paper reports on the development and field performance of an affordable multi-channel wireless data acquisition for IoT monitoring of environmental variations in berry orchards. A connectivity board was custom-designed based on the powerful dual-core 32-bit microcontroller with WiFi antenna and LoRa modulation at 868MHz. The objective was to verify the possibility of transmitting multiple sensor readings with lower power consumption while increasing the reliability and stability of wireless communication at long distances (over 1.7 km). Collected data from the wireless sensor was compared and found to be consistent with measurements of a data logger installed in the same locations. The presented paper highlights the advantages of LoRa sensors for digital agriculture and the experience in real-time monitoring of environmental parameters in berry orchards.
Development and Field Evaluation of a Multichannel LoRa Sensor for IoT Monitoring in Berry Orchards from Redmond R. Shamshiri
]]> 75 0 https://cdn.slidesharecdn.com/ss_thumbnails/shamshirigil2021lorasensors-210315235120-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SunBot: Autonomous Nursing Assistant for Emission-Free Berry Production, General Concepts and Framework /slideshow/sunbot-autonomous-nursing-assistant-for-emissionfree-berry-production-general-concepts-and-framework-197147261/197147261 2019finallandtechnikmanuscriptweltzienshamshiri-191124225331
This paper is dedicated to the general concept and simulation framework used to develop an autonomous electric tractor-mower combination. Thus enabling preliminary studies and experiments to construct a functional model of an autonomous electric tractor that is capable of sensing the environment, navigate in shrubbery orchards, identify and avoid obstacles. For this cause a distributed ROS-based framework has been designed mirroring the modular control architecture of the different ECUs and the main on-board controller. The applied V-REP, ROS simulation offers standard features such as hardware abstraction, low-level device control, commonly used functionalities, message-passing between processes, and package management. The framework reduces efforts in code and application development that can be shared by all sensors and actuators. The simulation framework was applied to tune algorithms, test and validate different sensing and control strategies.]]>
This paper is dedicated to the general concept and simulation framework used to develop an autonomous electric tractor-mower combination. Thus enabling preliminary studies and experiments to construct a functional model of an autonomous electric tractor that is capable of sensing the environment, navigate in shrubbery orchards, identify and avoid obstacles. For this cause a distributed ROS-based framework has been designed mirroring the modular control architecture of the different ECUs and the main on-board controller. The applied V-REP, ROS simulation offers standard features such as hardware abstraction, low-level device control, commonly used functionalities, message-passing between processes, and package management. The framework reduces efforts in code and application development that can be shared by all sensors and actuators. The simulation framework was applied to tune algorithms, test and validate different sensing and control strategies.]]> Sun, 24 Nov 2019 22:53:31 GMT /slideshow/sunbot-autonomous-nursing-assistant-for-emissionfree-berry-production-general-concepts-and-framework-197147261/197147261 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) SunBot: Autonomous Nursing Assistant for Emission-Free Berry Production, General Concepts and Framework RedmondRaminShamshir This paper is dedicated to the general concept and simulation framework used to develop an autonomous electric tractor-mower combination. Thus enabling preliminary studies and experiments to construct a functional model of an autonomous electric tractor that is capable of sensing the environment, navigate in shrubbery orchards, identify and avoid obstacles. For this cause a distributed ROS-based framework has been designed mirroring the modular control architecture of the different ECUs and the main on-board controller. The applied V-REP, ROS simulation offers standard features such as hardware abstraction, low-level device control, commonly used functionalities, message-passing between processes, and package management. The framework reduces efforts in code and application development that can be shared by all sensors and actuators. The simulation framework was applied to tune algorithms, test and validate different sensing and control strategies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2019finallandtechnikmanuscriptweltzienshamshiri-191124225331-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This paper is dedicated to the general concept and simulation framework used to develop an autonomous electric tractor-mower combination. Thus enabling preliminary studies and experiments to construct a functional model of an autonomous electric tractor that is capable of sensing the environment, navigate in shrubbery orchards, identify and avoid obstacles. For this cause a distributed ROS-based framework has been designed mirroring the modular control architecture of the different ECUs and the main on-board controller. The applied V-REP, ROS simulation offers standard features such as hardware abstraction, low-level device control, commonly used functionalities, message-passing between processes, and package management. The framework reduces efforts in code and application development that can be shared by all sensors and actuators. The simulation framework was applied to tune algorithms, test and validate different sensing and control strategies.
SunBot: Autonomous Nursing Assistant for Emission-Free Berry Production, General Concepts and Framework from Redmond R. Shamshiri
]]> 203 2 https://cdn.slidesharecdn.com/ss_thumbnails/2019finallandtechnikmanuscriptweltzienshamshiri-191124225331-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision /RedmondRaminShamshir/smart-management-of-oil-palm-plantations-with-autonomous-uav-imagery-and-robust-machine-vision slidesuavbreakthroughinoilpalmprecisionag-190907012457
A Breakthrough in Oil Palm Precision Agriculture: Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision Link to the publication https://www.researchgate.net/publication/306348123_A_Breakthrough_in_Oil_Palm_Precision_Agriculture_Smart_Management_of_Oil_Palm_Plantations_with_Autonomous_UAV_Imagery_and_Robust_Machine_Vision]]>
A Breakthrough in Oil Palm Precision Agriculture: Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision Link to the publication https://www.researchgate.net/publication/306348123_A_Breakthrough_in_Oil_Palm_Precision_Agriculture_Smart_Management_of_Oil_Palm_Plantations_with_Autonomous_UAV_Imagery_and_Robust_Machine_Vision]]> Sat, 07 Sep 2019 01:24:56 GMT /RedmondRaminShamshir/smart-management-of-oil-palm-plantations-with-autonomous-uav-imagery-and-robust-machine-vision RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision RedmondRaminShamshir A Breakthrough in Oil Palm Precision Agriculture: Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision Link to the publication https://www.researchgate.net/publication/306348123_A_Breakthrough_in_Oil_Palm_Precision_Agriculture_Smart_Management_of_Oil_Palm_Plantations_with_Autonomous_UAV_Imagery_and_Robust_Machine_Vision <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesuavbreakthroughinoilpalmprecisionag-190907012457-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A Breakthrough in Oil Palm Precision Agriculture: Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision Link to the publication https://www.researchgate.net/publication/306348123_A_Breakthrough_in_Oil_Palm_Precision_Agriculture_Smart_Management_of_Oil_Palm_Plantations_with_Autonomous_UAV_Imagery_and_Robust_Machine_Vision
Smart Management of Oil Palm Plantations with Autonomous UAV Imagery and Robust Machine Vision from Redmond R. Shamshiri
]]> 565 2 https://cdn.slidesharecdn.com/ss_thumbnails/slidesuavbreakthroughinoilpalmprecisionag-190907012457-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Digital Agriculture, Virtual reality, Agricultural Robotics /slideshow/digital-agriculture-virtual-reality-agricultural-robotics/124734553 virtualrealitydigitalfarming-181203023449
How robotics are used in digital agriculture]]>
How robotics are used in digital agriculture]]> Mon, 03 Dec 2018 02:34:49 GMT /slideshow/digital-agriculture-virtual-reality-agricultural-robotics/124734553 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Digital Agriculture, Virtual reality, Agricultural Robotics RedmondRaminShamshir How robotics are used in digital agriculture <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/virtualrealitydigitalfarming-181203023449-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> How robotics are used in digital agriculture
Digital Agriculture, Virtual reality, Agricultural Robotics from Redmond R. Shamshiri
]]> 1061 3 https://cdn.slidesharecdn.com/ss_thumbnails/virtualrealitydigitalfarming-181203023449-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Development of a Field Robot Platform for Mechanical Weed Control in Greenhouse Cultivation of Cucumber /RedmondRaminShamshir/development-of-a-field-robot-platform-for-mechanical-weed-control-in-greenhouse-cultivation-of-cucumber published2018weedcontrolrobot-181117131153
A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P < 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber.]]>
A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P < 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber.]]> Sat, 17 Nov 2018 13:11:53 GMT /RedmondRaminShamshir/development-of-a-field-robot-platform-for-mechanical-weed-control-in-greenhouse-cultivation-of-cucumber RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Development of a Field Robot Platform for Mechanical Weed Control in Greenhouse Cultivation of Cucumber RedmondRaminShamshir A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P < 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/published2018weedcontrolrobot-181117131153-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P &lt; 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber.
Development of a Field Robot Platform for Mechanical Weed Control in Greenhouse Cultivation of Cucumber from Redmond R. Shamshiri
]]> 206 3 https://cdn.slidesharecdn.com/ss_thumbnails/published2018weedcontrolrobot-181117131153-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Palm Oil Plantations /slideshow/fundamental-research-on-unmanned-aerial-vehicles-to-support-precision-agriculture-in-palm-oil-plantations/123275720 publishedshamshiri2018fundamentalresearchuav-181117125003
Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed.]]>
Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed.]]> Sat, 17 Nov 2018 12:50:03 GMT /slideshow/fundamental-research-on-unmanned-aerial-vehicles-to-support-precision-agriculture-in-palm-oil-plantations/123275720 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Palm Oil Plantations RedmondRaminShamshir Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/publishedshamshiri2018fundamentalresearchuav-181117125003-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed.
Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Palm Oil Plantations from Redmond R. Shamshiri
]]> 201 4 https://cdn.slidesharecdn.com/ss_thumbnails/publishedshamshiri2018fundamentalresearchuav-181117125003-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A perspective of Digital Farming, Robotics for Virtual Orchards /slideshow/a-perspective-of-digital-farming-robotics-for-virtual-orchards/119408317 redmondshamshiripresentationmcgberlin-181014215136
Research and Development in Agricultural Robotics: A perspective of Digital Farming Conference: TU-Berlin, Oct 2-3, 2018]]>
Research and Development in Agricultural Robotics: A perspective of Digital Farming Conference: TU-Berlin, Oct 2-3, 2018]]> Sun, 14 Oct 2018 21:51:36 GMT /slideshow/a-perspective-of-digital-farming-robotics-for-virtual-orchards/119408317 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) A perspective of Digital Farming, Robotics for Virtual Orchards RedmondRaminShamshir Research and Development in Agricultural Robotics: A perspective of Digital Farming Conference: TU-Berlin, Oct 2-3, 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redmondshamshiripresentationmcgberlin-181014215136-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Research and Development in Agricultural Robotics: A perspective of Digital Farming Conference: TU-Berlin, Oct 2-3, 2018
A perspective of Digital Farming, Robotics for Virtual Orchards from Redmond R. Shamshiri
]]> 264 4 https://cdn.slidesharecdn.com/ss_thumbnails/redmondshamshiripresentationmcgberlin-181014215136-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Research and development in agricultural robotics: A perspective of digital farming /slideshow/research-and-development-in-agricultural-robotics-a-perspective-of-digital-farming/109642294 publishedagriculturalroboticsdigitalfarming-180813050202
Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.]]>
Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.]]> Mon, 13 Aug 2018 05:02:02 GMT /slideshow/research-and-development-in-agricultural-robotics-a-perspective-of-digital-farming/109642294 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Research and development in agricultural robotics: A perspective of digital farming RedmondRaminShamshir Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/publishedagriculturalroboticsdigitalfarming-180813050202-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.
Research and development in agricultural robotics: A perspective of digital farming from Redmond R. Shamshiri
]]> 1028 8 https://cdn.slidesharecdn.com/ss_thumbnails/publishedagriculturalroboticsdigitalfarming-180813050202-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 An Overview of the System of Rice Intensification for Paddy Fields of Malaysia /slideshow/an-overview-of-the-system-of-rice-intensification-for-paddy-fields-of-malaysia-97359764/97359764 overviewsrishamshirietalmay2018-180517030413
Objectives: The objective of this paper was to present a general overview of rice agronomic practices and transplanting operations by considering the interactions of soil, plant, and machine relationship in line with the System of Rice Intensification (SRI) cultivation practice. Methods: Some of the problems challenging Malaysian rice growers, as well as yield increase and total rice production in the last four decades, were first addressed and discussed. The trend in the world rice production between 1961 and 2014 was used to predict the production in 2020 and to show that Southeast Asian countries are expected to increase their production by 27.2%. Findings: A consistently increasing pattern from 3.1 tons/ha during 1981 to 4.1 tons/ha in 2014 was observed in the rice yield of Malaysia due to the advances in technology and improved farming operations coupled with integrated management and control of resources. Various literature were reviewed and their findings of the best transplanting practices were summarized to discuss how SRI contributes to the production of higher rice yield with improved transplanting practices through a more effective root system. Our review shows that wider spacing, availability of solar radiation, medium temperature, soil aeration, and nutrient supply promote shorter Phyllochrons which increase the number of tillers in rice. In this regard, modification and development of a transplanter that complies with SRI specification require determination of optimum transplanting spacing, seed rate, and planting pattern to significantly improve yield. Improvement: It was concluded that for maximum yield, the SRI method in Malaysia should emphasize on the planting of one seedling per hill with space of 0.25 m for optimum water consumption, nutrient and pest management.]]>
Objectives: The objective of this paper was to present a general overview of rice agronomic practices and transplanting operations by considering the interactions of soil, plant, and machine relationship in line with the System of Rice Intensification (SRI) cultivation practice. Methods: Some of the problems challenging Malaysian rice growers, as well as yield increase and total rice production in the last four decades, were first addressed and discussed. The trend in the world rice production between 1961 and 2014 was used to predict the production in 2020 and to show that Southeast Asian countries are expected to increase their production by 27.2%. Findings: A consistently increasing pattern from 3.1 tons/ha during 1981 to 4.1 tons/ha in 2014 was observed in the rice yield of Malaysia due to the advances in technology and improved farming operations coupled with integrated management and control of resources. Various literature were reviewed and their findings of the best transplanting practices were summarized to discuss how SRI contributes to the production of higher rice yield with improved transplanting practices through a more effective root system. Our review shows that wider spacing, availability of solar radiation, medium temperature, soil aeration, and nutrient supply promote shorter Phyllochrons which increase the number of tillers in rice. In this regard, modification and development of a transplanter that complies with SRI specification require determination of optimum transplanting spacing, seed rate, and planting pattern to significantly improve yield. Improvement: It was concluded that for maximum yield, the SRI method in Malaysia should emphasize on the planting of one seedling per hill with space of 0.25 m for optimum water consumption, nutrient and pest management.]]> Thu, 17 May 2018 03:04:12 GMT /slideshow/an-overview-of-the-system-of-rice-intensification-for-paddy-fields-of-malaysia-97359764/97359764 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) An Overview of the System of Rice Intensification for Paddy Fields of Malaysia RedmondRaminShamshir Objectives: The objective of this paper was to present a general overview of rice agronomic practices and transplanting operations by considering the interactions of soil, plant, and machine relationship in line with the System of Rice Intensification (SRI) cultivation practice. Methods: Some of the problems challenging Malaysian rice growers, as well as yield increase and total rice production in the last four decades, were first addressed and discussed. The trend in the world rice production between 1961 and 2014 was used to predict the production in 2020 and to show that Southeast Asian countries are expected to increase their production by 27.2%. Findings: A consistently increasing pattern from 3.1 tons/ha during 1981 to 4.1 tons/ha in 2014 was observed in the rice yield of Malaysia due to the advances in technology and improved farming operations coupled with integrated management and control of resources. Various literature were reviewed and their findings of the best transplanting practices were summarized to discuss how SRI contributes to the production of higher rice yield with improved transplanting practices through a more effective root system. Our review shows that wider spacing, availability of solar radiation, medium temperature, soil aeration, and nutrient supply promote shorter Phyllochrons which increase the number of tillers in rice. In this regard, modification and development of a transplanter that complies with SRI specification require determination of optimum transplanting spacing, seed rate, and planting pattern to significantly improve yield. Improvement: It was concluded that for maximum yield, the SRI method in Malaysia should emphasize on the planting of one seedling per hill with space of 0.25 m for optimum water consumption, nutrient and pest management. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/overviewsrishamshirietalmay2018-180517030413-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Objectives: The objective of this paper was to present a general overview of rice agronomic practices and transplanting operations by considering the interactions of soil, plant, and machine relationship in line with the System of Rice Intensification (SRI) cultivation practice. Methods: Some of the problems challenging Malaysian rice growers, as well as yield increase and total rice production in the last four decades, were first addressed and discussed. The trend in the world rice production between 1961 and 2014 was used to predict the production in 2020 and to show that Southeast Asian countries are expected to increase their production by 27.2%. Findings: A consistently increasing pattern from 3.1 tons/ha during 1981 to 4.1 tons/ha in 2014 was observed in the rice yield of Malaysia due to the advances in technology and improved farming operations coupled with integrated management and control of resources. Various literature were reviewed and their findings of the best transplanting practices were summarized to discuss how SRI contributes to the production of higher rice yield with improved transplanting practices through a more effective root system. Our review shows that wider spacing, availability of solar radiation, medium temperature, soil aeration, and nutrient supply promote shorter Phyllochrons which increase the number of tillers in rice. In this regard, modification and development of a transplanter that complies with SRI specification require determination of optimum transplanting spacing, seed rate, and planting pattern to significantly improve yield. Improvement: It was concluded that for maximum yield, the SRI method in Malaysia should emphasize on the planting of one seedling per hill with space of 0.25 m for optimum water consumption, nutrient and pest management.
An Overview of the System of Rice Intensification for Paddy Fields of Malaysia from Redmond R. Shamshiri
]]> 698 5 https://cdn.slidesharecdn.com/ss_thumbnails/overviewsrishamshirietalmay2018-180517030413-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB /slideshow/robotic-harvesting-of-fruiting-vegetables-a-simulation-approach-in-vrep-ros-and-matlab/90637780 redmondroboticsimulationbookchapter-180314115700
In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose output were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish and subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.]]>
In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose output were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish and subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.]]> Wed, 14 Mar 2018 11:57:00 GMT /slideshow/robotic-harvesting-of-fruiting-vegetables-a-simulation-approach-in-vrep-ros-and-matlab/90637780 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB RedmondRaminShamshir In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose output were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish and subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redmondroboticsimulationbookchapter-180314115700-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose output were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish and subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.
Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB from Redmond R. Shamshiri
]]> 402 5 https://cdn.slidesharecdn.com/ss_thumbnails/redmondroboticsimulationbookchapter-180314115700-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals /slideshow/choosing-the-best-uav-drones-for-precision-agriculture-and-smart-farming-agricultural-drone-buyers-guide-for-farmers-and-agriculture-service-professionals/86115524 2017bestdroneuavforagricultureredmondraminshamshiri-180113192855
Best Drones For Agriculture, Exploring agricultural drones, Agricultural Drone Technology, Agricultural Drones for Sale, Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals]]>
Best Drones For Agriculture, Exploring agricultural drones, Agricultural Drone Technology, Agricultural Drones for Sale, Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals]]> Sat, 13 Jan 2018 19:28:55 GMT /slideshow/choosing-the-best-uav-drones-for-precision-agriculture-and-smart-farming-agricultural-drone-buyers-guide-for-farmers-and-agriculture-service-professionals/86115524 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals RedmondRaminShamshir Best Drones For Agriculture, Exploring agricultural drones, Agricultural Drone Technology, Agricultural Drones for Sale, Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017bestdroneuavforagricultureredmondraminshamshiri-180113192855-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Best Drones For Agriculture, Exploring agricultural drones, Agricultural Drone Technology, Agricultural Drones for Sale, Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals
Choosing the Best UAV Drones for Precision Agriculture and Smart Farming: Agricultural drone buyers guide for farmers and agriculture service professionals from Redmond R. Shamshiri
]]> 5772 19 https://cdn.slidesharecdn.com/ss_thumbnails/2017bestdroneuavforagricultureredmondraminshamshiri-180113192855-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture /slideshow/adaptive-agrotech-report-2017-visiting-autogrow-intelligent-automation-for-indoor-agriculture/85872352 2017adaptiveagrptechreportautogrowvisit-180108203744
Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture]]>
Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture]]> Mon, 08 Jan 2018 20:37:44 GMT /slideshow/adaptive-agrotech-report-2017-visiting-autogrow-intelligent-automation-for-indoor-agriculture/85872352 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture RedmondRaminShamshir Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017adaptiveagrptechreportautogrowvisit-180108203744-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture
Adaptive AgroTech Report 2017: visiting AutoGrow Intelligent Automation for Indoor Agriculture from Redmond R. Shamshiri
]]> 136 1 https://cdn.slidesharecdn.com/ss_thumbnails/2017adaptiveagrptechreportautogrowvisit-180108203744-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 An Introduction to Controlled Greenhouse Plant Production Systems For Tropical Lowlands, A lecture handout /slideshow/an-introduction-to-controlled-greenhouse-plant-production-systems-for-tropical-lowlands-a-lecture-handout/85364564 introductiontogreenhouseproductionintropicsslides-171230093341
Introduction, Greenhouses in the Netherlands vs. Malaysia, Some Definitions, History, Simple fact yet ignored, Concept of Adaptive Solution applied to Greenhouse, Introduction to Greenhouse Automation and Control System Engineering, Open-field vs. Closed-field, Vegetable production in the highlands and lowlands of Malaysia Malaysian Strategy and Policy on Vegetable Production (2011-2020)]]>
Introduction, Greenhouses in the Netherlands vs. Malaysia, Some Definitions, History, Simple fact yet ignored, Concept of Adaptive Solution applied to Greenhouse, Introduction to Greenhouse Automation and Control System Engineering, Open-field vs. Closed-field, Vegetable production in the highlands and lowlands of Malaysia Malaysian Strategy and Policy on Vegetable Production (2011-2020)]]> Sat, 30 Dec 2017 09:33:41 GMT /slideshow/an-introduction-to-controlled-greenhouse-plant-production-systems-for-tropical-lowlands-a-lecture-handout/85364564 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) An Introduction to Controlled Greenhouse Plant Production Systems For Tropical Lowlands, A lecture handout RedmondRaminShamshir Introduction, Greenhouses in the Netherlands vs. Malaysia, Some Definitions, History, Simple fact yet ignored, Concept of Adaptive Solution applied to Greenhouse, Introduction to Greenhouse Automation and Control System Engineering, Open-field vs. Closed-field, Vegetable production in the highlands and lowlands of Malaysia Malaysian Strategy and Policy on Vegetable Production (2011-2020) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontogreenhouseproductionintropicsslides-171230093341-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Introduction, Greenhouses in the Netherlands vs. Malaysia, Some Definitions, History, Simple fact yet ignored, Concept of Adaptive Solution applied to Greenhouse, Introduction to Greenhouse Automation and Control System Engineering, Open-field vs. Closed-field, Vegetable production in the highlands and lowlands of Malaysia Malaysian Strategy and Policy on Vegetable Production (2011-2020)
An Introduction to Controlled Greenhouse Plant Production Systems For Tropical Lowlands, A lecture handout from Redmond R. Shamshiri
]]> 618 3 https://cdn.slidesharecdn.com/ss_thumbnails/introductiontogreenhouseproductionintropicsslides-171230093341-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Acceleration of Robotic Harvesting by Simulation /slideshow/acceleration-of-robotic-harvesting-by-simulation/84890336 roboticredmondposterfinal-171225064328
Acceleration of Robotic Harvesting by Simulation]]>
Acceleration of Robotic Harvesting by Simulation]]> Mon, 25 Dec 2017 06:43:28 GMT /slideshow/acceleration-of-robotic-harvesting-by-simulation/84890336 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Acceleration of Robotic Harvesting by Simulation RedmondRaminShamshir Acceleration of Robotic Harvesting by Simulation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/roboticredmondposterfinal-171225064328-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Acceleration of Robotic Harvesting by Simulation
Acceleration of Robotic Harvesting by Simulation from Redmond R. Shamshiri
]]> 127 3 https://cdn.slidesharecdn.com/ss_thumbnails/roboticredmondposterfinal-171225064328-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation /RedmondRaminShamshir/robotic-harvesting-of-fruiting-vegetables-acceleration-by-simulation-80247099 2017redmondraminshamshiriroboticharvestingforpdf1-170928084449
Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation Presented at the Acceleration Workshop Robotics & Crop Sensing in Greenhouses, 11-12 September 2017. Delf University of Technology, The Netherlands Robotic Harvesting with NOVABOT innovative manipulator https://youtu.be/R38IoVcOVt0 Robotic Harvesting with multiple SCARA manipulators https://youtu.be/TLLW3S-55ls Robotic Harvesting with Array of Linear Actuators https://youtu.be/iFu7FAxLvmg Robotic Harvesting with fanuc lr mate 200id (Visual Servo Control in V-REP, ROS, MATLAB) https://youtu.be/BwRBUeB812s Robotic Harvesting, Simulation of Environment and Fruit/Plant Scan https://youtu.be/XD3J7b0cDGM Advanced Visual Servo Control in V-REP for Robotic harvesting of sweet pepper https://youtu.be/VupoirQOL0Y Robotic Harvesting of Sweet Pepper, Ubuntu, V-REP, ROS Environment Setup https://youtu.be/tKagjNQ9FW4 Real-time, robust and rapid red-pepper fruit detection with Matlab https://youtu.be/rFV6Y5ivLF8 Talk https://youtu.be/QZawPeg3wEQ]]>
Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation Presented at the Acceleration Workshop Robotics & Crop Sensing in Greenhouses, 11-12 September 2017. Delf University of Technology, The Netherlands Robotic Harvesting with NOVABOT innovative manipulator https://youtu.be/R38IoVcOVt0 Robotic Harvesting with multiple SCARA manipulators https://youtu.be/TLLW3S-55ls Robotic Harvesting with Array of Linear Actuators https://youtu.be/iFu7FAxLvmg Robotic Harvesting with fanuc lr mate 200id (Visual Servo Control in V-REP, ROS, MATLAB) https://youtu.be/BwRBUeB812s Robotic Harvesting, Simulation of Environment and Fruit/Plant Scan https://youtu.be/XD3J7b0cDGM Advanced Visual Servo Control in V-REP for Robotic harvesting of sweet pepper https://youtu.be/VupoirQOL0Y Robotic Harvesting of Sweet Pepper, Ubuntu, V-REP, ROS Environment Setup https://youtu.be/tKagjNQ9FW4 Real-time, robust and rapid red-pepper fruit detection with Matlab https://youtu.be/rFV6Y5ivLF8 Talk https://youtu.be/QZawPeg3wEQ]]> Thu, 28 Sep 2017 08:44:49 GMT /RedmondRaminShamshir/robotic-harvesting-of-fruiting-vegetables-acceleration-by-simulation-80247099 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation RedmondRaminShamshir Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation Presented at the Acceleration Workshop Robotics & Crop Sensing in Greenhouses, 11-12 September 2017. Delf University of Technology, The Netherlands Robotic Harvesting with NOVABOT innovative manipulator https://youtu.be/R38IoVcOVt0 Robotic Harvesting with multiple SCARA manipulators https://youtu.be/TLLW3S-55ls Robotic Harvesting with Array of Linear Actuators https://youtu.be/iFu7FAxLvmg Robotic Harvesting with fanuc lr mate 200id (Visual Servo Control in V-REP, ROS, MATLAB) https://youtu.be/BwRBUeB812s Robotic Harvesting, Simulation of Environment and Fruit/Plant Scan https://youtu.be/XD3J7b0cDGM Advanced Visual Servo Control in V-REP for Robotic harvesting of sweet pepper https://youtu.be/VupoirQOL0Y Robotic Harvesting of Sweet Pepper, Ubuntu, V-REP, ROS Environment Setup https://youtu.be/tKagjNQ9FW4 Real-time, robust and rapid red-pepper fruit detection with Matlab https://youtu.be/rFV6Y5ivLF8 Talk https://youtu.be/QZawPeg3wEQ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017redmondraminshamshiriroboticharvestingforpdf1-170928084449-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation Presented at the Acceleration Workshop Robotics &amp; Crop Sensing in Greenhouses, 11-12 September 2017. Delf University of Technology, The Netherlands Robotic Harvesting with NOVABOT innovative manipulator https://youtu.be/R38IoVcOVt0 Robotic Harvesting with multiple SCARA manipulators https://youtu.be/TLLW3S-55ls Robotic Harvesting with Array of Linear Actuators https://youtu.be/iFu7FAxLvmg Robotic Harvesting with fanuc lr mate 200id (Visual Servo Control in V-REP, ROS, MATLAB) https://youtu.be/BwRBUeB812s Robotic Harvesting, Simulation of Environment and Fruit/Plant Scan https://youtu.be/XD3J7b0cDGM Advanced Visual Servo Control in V-REP for Robotic harvesting of sweet pepper https://youtu.be/VupoirQOL0Y Robotic Harvesting of Sweet Pepper, Ubuntu, V-REP, ROS Environment Setup https://youtu.be/tKagjNQ9FW4 Real-time, robust and rapid red-pepper fruit detection with Matlab https://youtu.be/rFV6Y5ivLF8 Talk https://youtu.be/QZawPeg3wEQ
Robotic Harvesting of Fruiting Vegetables, Acceleration by Simulation from Redmond R. Shamshiri
]]> 1459 6 https://cdn.slidesharecdn.com/ss_thumbnails/2017redmondraminshamshiriroboticharvestingforpdf1-170928084449-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dynamic Assessment of Air Temperature for Tomato (Lycopersicon Esculentum) Cultivation in a Naturally Ventilated Net-Screen Greenhouse under Tropical Lowlands Climate /slideshow/dynamic-assessment-of-air-temperature-for-tomato-lycopersicon-esculentum-cultivation-in-a-naturally-ventilated-netscreen-greenhouse-under-tropical-lowlands-climate/70249681 shamshirietaldec182016-161218205118
Net-screen covered greenhouses operating on natural ventilation are used as a sustainable approach for closed-field cultivation of fruits and vegetables and to eliminate insect passage and subsequent production damage. The objective of this work was to develop a real-time assessment framework for evaluating air-temperature inside an insect-proof net-screen greenhouse in tropical lowlands of Malaysia prior to cultivation of tomato. Mathematical description of a growth response model was implemented and used in a computer application. A custom-designed data acquisition system was built for collecting 6 months of air-temperature data, during July to December 2014. For each measured air-temperature (T), an optimality degree, denoted by Opt(T), was calculated with respect to different light conditions (sun, cloud, night) and different growth stages. Interactive three-dimensional plots were generated to demonstrate variations in Opt(T) values due to different hours and days in a growth season. Results showed that, air temperature was never less than 25% optimal for early growth, and 51% for vegetative to mature fruiting growth stages. The average Opt(T) in the entire 6 months was between 65 and 75%. The presented framework allows tomato growers to automatically collect and process raw air temperature data and to simulate growth responses at different growth stages and light conditions. The software database can be used to track and record Opt(T) values from any greenhouses with different structure design, covering materials, cooling system and growing seasons, and to contribute to knowledge-based decision support systems and energy balance models.]]>
Net-screen covered greenhouses operating on natural ventilation are used as a sustainable approach for closed-field cultivation of fruits and vegetables and to eliminate insect passage and subsequent production damage. The objective of this work was to develop a real-time assessment framework for evaluating air-temperature inside an insect-proof net-screen greenhouse in tropical lowlands of Malaysia prior to cultivation of tomato. Mathematical description of a growth response model was implemented and used in a computer application. A custom-designed data acquisition system was built for collecting 6 months of air-temperature data, during July to December 2014. For each measured air-temperature (T), an optimality degree, denoted by Opt(T), was calculated with respect to different light conditions (sun, cloud, night) and different growth stages. Interactive three-dimensional plots were generated to demonstrate variations in Opt(T) values due to different hours and days in a growth season. Results showed that, air temperature was never less than 25% optimal for early growth, and 51% for vegetative to mature fruiting growth stages. The average Opt(T) in the entire 6 months was between 65 and 75%. The presented framework allows tomato growers to automatically collect and process raw air temperature data and to simulate growth responses at different growth stages and light conditions. The software database can be used to track and record Opt(T) values from any greenhouses with different structure design, covering materials, cooling system and growing seasons, and to contribute to knowledge-based decision support systems and energy balance models.]]> Sun, 18 Dec 2016 20:51:18 GMT /slideshow/dynamic-assessment-of-air-temperature-for-tomato-lycopersicon-esculentum-cultivation-in-a-naturally-ventilated-netscreen-greenhouse-under-tropical-lowlands-climate/70249681 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Dynamic Assessment of Air Temperature for Tomato (Lycopersicon Esculentum) Cultivation in a Naturally Ventilated Net-Screen Greenhouse under Tropical Lowlands Climate RedmondRaminShamshir Net-screen covered greenhouses operating on natural ventilation are used as a sustainable approach for closed-field cultivation of fruits and vegetables and to eliminate insect passage and subsequent production damage. The objective of this work was to develop a real-time assessment framework for evaluating air-temperature inside an insect-proof net-screen greenhouse in tropical lowlands of Malaysia prior to cultivation of tomato. Mathematical description of a growth response model was implemented and used in a computer application. A custom-designed data acquisition system was built for collecting 6 months of air-temperature data, during July to December 2014. For each measured air-temperature (T), an optimality degree, denoted by Opt(T), was calculated with respect to different light conditions (sun, cloud, night) and different growth stages. Interactive three-dimensional plots were generated to demonstrate variations in Opt(T) values due to different hours and days in a growth season. Results showed that, air temperature was never less than 25% optimal for early growth, and 51% for vegetative to mature fruiting growth stages. The average Opt(T) in the entire 6 months was between 65 and 75%. The presented framework allows tomato growers to automatically collect and process raw air temperature data and to simulate growth responses at different growth stages and light conditions. The software database can be used to track and record Opt(T) values from any greenhouses with different structure design, covering materials, cooling system and growing seasons, and to contribute to knowledge-based decision support systems and energy balance models. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/shamshirietaldec182016-161218205118-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Net-screen covered greenhouses operating on natural ventilation are used as a sustainable approach for closed-field cultivation of fruits and vegetables and to eliminate insect passage and subsequent production damage. The objective of this work was to develop a real-time assessment framework for evaluating air-temperature inside an insect-proof net-screen greenhouse in tropical lowlands of Malaysia prior to cultivation of tomato. Mathematical description of a growth response model was implemented and used in a computer application. A custom-designed data acquisition system was built for collecting 6 months of air-temperature data, during July to December 2014. For each measured air-temperature (T), an optimality degree, denoted by Opt(T), was calculated with respect to different light conditions (sun, cloud, night) and different growth stages. Interactive three-dimensional plots were generated to demonstrate variations in Opt(T) values due to different hours and days in a growth season. Results showed that, air temperature was never less than 25% optimal for early growth, and 51% for vegetative to mature fruiting growth stages. The average Opt(T) in the entire 6 months was between 65 and 75%. The presented framework allows tomato growers to automatically collect and process raw air temperature data and to simulate growth responses at different growth stages and light conditions. The software database can be used to track and record Opt(T) values from any greenhouses with different structure design, covering materials, cooling system and growing seasons, and to contribute to knowledge-based decision support systems and energy balance models.
Dynamic Assessment of Air Temperature for Tomato (Lycopersicon Esculentum) Cultivation in a Naturally Ventilated Net-Screen Greenhouse under Tropical Lowlands Climate from Redmond R. Shamshiri
]]> 320 2 https://cdn.slidesharecdn.com/ss_thumbnails/shamshirietaldec182016-161218205118-thumbnail.jpg?width=120&height=120&fit=bounds document 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Global Positioning System in Precision Agriculture /slideshow/global-positioning-system-in-precision-agriculture/70233620 redmondraminshamshirigpsinprecisionag-161217183541
Global Positioning System in Precision Agriculture]]>
Global Positioning System in Precision Agriculture]]> Sat, 17 Dec 2016 18:35:41 GMT /slideshow/global-positioning-system-in-precision-agriculture/70233620 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Global Positioning System in Precision Agriculture RedmondRaminShamshir Global Positioning System in Precision Agriculture <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redmondraminshamshirigpsinprecisionag-161217183541-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Global Positioning System in Precision Agriculture
Global Positioning System in Precision Agriculture from Redmond R. Shamshiri
]]> 1189 2 https://cdn.slidesharecdn.com/ss_thumbnails/redmondraminshamshirigpsinprecisionag-161217183541-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Analysis of reflectance spectra for identification of vegetation substance /slideshow/analysis-of-reflectance-spectra-for-identification-of-vegetation-substance/70233084 redmondraminshamshiri2016analysisreflectance-161217180115
A lecture note on Analysis of reflectance spectra for identification of vegetation substance Redmond Ramin Shamshiri, PhD ramin.sh@ufl.edu ]]>
A lecture note on Analysis of reflectance spectra for identification of vegetation substance Redmond Ramin Shamshiri, PhD ramin.sh@ufl.edu ]]> Sat, 17 Dec 2016 18:01:15 GMT /slideshow/analysis-of-reflectance-spectra-for-identification-of-vegetation-substance/70233084 RedmondRaminShamshir@slideshare.net(RedmondRaminShamshir) Analysis of reflectance spectra for identification of vegetation substance RedmondRaminShamshir A lecture note on Analysis of reflectance spectra for identification of vegetation substance Redmond Ramin Shamshiri, PhD ramin.sh@ufl.edu <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redmondraminshamshiri2016analysisreflectance-161217180115-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A lecture note on Analysis of reflectance spectra for identification of vegetation substance Redmond Ramin Shamshiri, PhD ramin.sh@ufl.edu
Analysis of reflectance spectra for identification of vegetation substance from Redmond R. Shamshiri
]]> 182 2 https://cdn.slidesharecdn.com/ss_thumbnails/redmondraminshamshiri2016analysisreflectance-161217180115-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/shamshirigil2021lorasensors-210315235120-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/development-and-field-evaluation-of-a-multichannel-lora-sensor-for-iot-monitoring-in-berry-orchards/244457225 Development and Field ... https://cdn.slidesharecdn.com/ss_thumbnails/2019finallandtechnikmanuscriptweltzienshamshiri-191124225331-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/sunbot-autonomous-nursing-assistant-for-emissionfree-berry-production-general-concepts-and-framework-197147261/197147261 SunBot: Autonomous Nur... https://cdn.slidesharecdn.com/ss_thumbnails/slidesuavbreakthroughinoilpalmprecisionag-190907012457-thumbnail.jpg?width=320&height=320&fit=bounds RedmondRaminShamshir/smart-management-of-oil-palm-plantations-with-autonomous-uav-imagery-and-robust-machine-vision Smart Management of Oi...