CROP DISEASE DETECTION USING MACHINE LEARNING: CASE STUDY AAUA FARM

In recent years, the application of machine learning techniques in agriculture has gained substantial attention due to their potential to revolutionize crop management and yield optimization. Crop diseases pose a significant threat to agricultural production, leading to substantial economic losses and food security challenges. Addressing this issue requires accurate and timely disease detection methods. This research project focuses on the development of a crop disease detection system using machine learning, with a specific case study conducted at AAUA Farm. The system aims to leverage the power of machine learning algorithms to identify and classify various crop diseases affecting the farm's produce. One of the primary challenges in crop disease detection lies in the ability to differentiate between healthy and infected plants accurately. Machine learning techniques offer the promise of automating this process, allowing for early disease identification and prompt intervention. The choice of AAUA Farm as the case study site is driven by the need for real-world validation and the opportunity to tailor the system to the unique conditions of the farm. By utilizing machine learning algorithms, such as convolutional neural networks (CNNs) or support vector machines (SVMs), this research seeks to achieve a high level of accuracy in disease detection while considering factors such as lighting conditions, plant varieties, and disease types.