Privacy-Aware AI-Based Agricultural Monitoring Using Internet of Drones

Artificial Intelligence (AI) has become a vital tool for agricultural farming. AI-based image processing models utilizing different machine learning (ML) algorithms and deep learning (DL) offer advanced functionalities in disease detection, yield estimation, land use, etc. This thesis examines AI-driven techniques utilizing Convolutional Neural Networks (CNN) with the addition of Federated Learning (FL) to analyze satellite and drone images for agricultural insights, especially in detecting Cotton diseases. The AI models improve agricultural farming in many ways, such as using data to make critical decisions, reducing labor costs, pest infestations, etc. Moreover, these models allow farmers to minimize yield losses by making early interventions. AI models can also efficiently use land by analyzing soil conditions and crop performance. This thesis utilizes various ML models and an FL model for classifying cotton plant diseases. In addition, we used an authentication scheme to collect the images securely. We received accuracy scores of over 90%. The results are promising; however, future work should focus on validating that the results are good on new and unseen data. This will ensure that the model is robust.