Ibrahim A. Hameed

By Redmond R. Shamshiri, Ibrahim A. Hameed, Manoj Karkee and Cornelia Weltzien

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 outputs 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.

Part of the book: Automation in Agriculture

By Amid Heravi, Desa Ahmad, Ibrahim A. Hameed, Redmond Ramin Shamshiri, Siva K. Balasundram and Muhammad Yamin

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.

Part of the book: Agricultural Robots

By Redmond Ramin Shamshiri, Ibrahim A. Hameed, Siva K. Balasundram, Desa Ahmad, Cornelia Weltzien and Muhammad Yamin

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.

Part of the book: Agricultural Robots