Adaptive Super Twisting Sliding Mode Controller Design of Quadcopter for Wheat Disease Detection
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Date
2023-11
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Addis Ababa University
Abstract
Brown wheat rust is a fungal disease that can cause huge destruction in wheat production and
quality. Collecting accurate large scale crop data and detecting these diseases based on certain
standards through visual inspection is labor intensive, time consuming, and prone to human
error. This paper focuses on the design of adaptive super twisting sliding mode controller of a
quadcopter for detection of brown wheat rust disease. First, the dynamics of the system was
understood then the Newton-quaternion approach was used to model the dynamic system
and verified in simulink. Then, the adaptive super twisting sliding mode controller was
developed for attitude and position trajectory tracking of a quadrotor. Controller design
involves tuning the parameters of the supertwising sliding mode controller using adaptation
laws. Comparison of conventional sliding mode controller with the adaptive super twisting
sliding mode controller was analyzed. The effectiveness of the proposed control scheme
has been verified by developing simulation results for quadcopter in MATLAB/SIMULINK
software. The results show high tracking accuracy, chattering reduction, and disturbance
rejection capability of the proposed controller. For the task of brown wheat rust detection,
transfer learning technique was applied using the state of the art ResNet152v2 model to
perform feature extraction for the convolutional neural network architecture. The trained
model achieved an accuracy level of 93.28% in the training phase and 92% in the test set.
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Keywords
Brown rust, Quadcopter, Adaptive super twisting sliding mode control, ResNet152v2, Convolutional neural network.