Lebsework Negash (PhD)Nardos Belay2024-03-122024-03-122023-11https://etd.aau.edu.et/handle/123456789/2363Brown 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.en-USBrown rustQuadcopterAdaptive super twisting sliding mode controlResNet152v2Convolutional neural network.Thesis