Crop monitoring is frequently used by crop scientists to observe growth of plants and to relate plants phenotypes to their genotypes. Instead of traditional crop monitoring, which is labor intensive, high-throughput plant phenotyping (HTP) platforms using ground-based vehicle have several advantages over manual methods. Existing HTP platforms with robot arms has limited reach and payload, and they are sometimes not appropriate for monitoring large fields. In this research, a 5-DOF robot arm is developed and analyzed for monitoring several crops growth. This robot arm is a hybrid of both prismatic and rotational joints. This new mobile manipulator is light, has a compact structure, suitable for plant phenotyping, and doesn’t exist commercially. To investigate the performance of the robot arm, kinematics and dynamics analyses using Newton-Euler iterative method and MATLAB simulations are performed. Results matched with each other very well.

Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile, field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, and vegetation indices and is able to take high quality photos of crops. The system includes. developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.

Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, vegetation indices and is able to take high quality photos of crops. The system includes developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.

This paper proposes a new method for designing a state feedback controller for stabilizing and also making the desired performance of Interval Type-2 Takagi-Sugeno-Kang Fuzzy Logic Systems (fIT2 TSK FLS). By using the advantages of WU-Mendel Uncertainty Bounds (WM-UB) method, this paper proposes a new approach for Single-Input and Single-Output (SISO) and Multi-Input and Multi-Output (MIMO) interval type-2 TSK systems, which is based on the Hybrid Compensation Control (HCC) approach. The advantages of this method are no necessity to solving any Linear Matrix Inequalities (LMIs) to find a quadratic Lyapunov function for designing the stabilizer controller and also, the designed controller could compensate the time-varying variations. It should be noted that the inference engine is formulated in closed form and does not require using any type of reduction. Some examples have been conducted in our study to demonstrate the effectiveness of the proposed control design approach and compare this method with the previous approaches. All results illustrate good control performance.

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