Development of an Automated Crop Disease Detection System

Abstract

Crop diseases pose a significant threat to global food security, necessitating innovative approaches for early detection and intervention. This abstract presents an application that uses advanced machine learning algorithms to accurately identify and monitor diseases in crops. This imagery captures various spectral signatures, which are subsequently processed and analyzed to detect anomalies indicative of crop diseases. Image segmentation techniques are employed to separate healthy and diseased areas within the images, allowing for precise disease mapping. Machine learning plays a pivotal role in such applications by enabling automated disease recognition. Supervised learning models, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are trained on labeled datasets containing a wide range of crop disease instances. These models learn to distinguish between healthy and infected crops based on the extracted features and spectral signatures, achieving high accuracy and minimizing false positives. Real-time monitoring is a core feature, enabling farmers and agricultural stakeholders to receive timely disease alerts. The system's user-friendly interface, accessible through web applications, provides actionable insights and recommendations for targeted interventions. This empowers farmers with the information needed to implement precision agriculture practices and adopt integrated pest management strategies, optimizing crop yields while minimizing the use of pesticides. By offering early disease detection and predictive modeling capabilities, the system supports sustainable and resilient agriculture, contributing to global food security efforts.