Published Paper Details:

Abstract The rapid advancement of artificial intelligence (AI) and unmanned aerial vehicles, commonly known as drones, has opened new possibilities for precision agriculture. This paper presents a novel approach for the detection of plant diseases using AI-based drone technology. The system leverages high-resolution aerial imagery captured by drones and employs advanced machine learning algorithms to identify and classify various plant diseases in real-time. The use of unmanned aerial vehicles is revolutionizing the agricultural industry. Cashews are grown by approximately 70% of small and marginal farmers, and the cashew industry plays a critical role in their economic development. Several fungal and algal diseases threaten the cashew tree, resulting in substantial losses in yield. Some cashew-growing regions are particularly susceptible to powdery mildew, damping off, anthracnose, and inflorescence blight. To take timely countermeasures against plant diseases and infections, it is imperative to monitor and detect diseases as early as possible and take suitable measures. Using, such as those that are equipped with artificial intelligence, can assist farmers by providing early detection of crop diseases and precision pesticide application. To facilitate efficient and effective crop monitoring, a equipped with a camera will be deployed to take aerial photographs. An edge computing paradigm of Artificial Intelligence is employed to process this image in order to make decisions with the least amount of latency possible. This paper discusses the system architecture, including the drone hardware, imaging sensors, and the AI algorithms used for image processing. It also addresses implementation challenges such as varying environmental conditions and the need for large annotated datasets for training the AI models. Preliminary field trials demonstrate the system's effectiveness in detecting common plant diseases, with promising results that suggest significant potential for improving crop management and yield. Future work will focus on expanding the range of detectable diseases, enhancing the robustness of the AI models, and integrating the system with existing agricultural management platforms.