LEAF BLIGHT DETECTION USING DEEP LEARNING

Abstract

Plant leaf blight is a common disease that affects many crops, causing significant economic losses in agriculture. Early detection of blight is essential for farmers to prevent the spread of the disease, control its severity, and improve crop yields. Manual detection of blight is time-consuming and often unreliable, making it necessary to use advanced technologies for accurate and efficient detection. Plant leaf blight detection systems can help farmers monitor their crops in real-time and identify blight at an early stage. This enables farmers to take appropriate measures such as crop rotation, timely application of fungicides, and improving plant nutrition to prevent the spread of the disease. By using plant leaf blight detection systems, farmers can reduce the economic losses caused by the disease and ensure the availability of healthy crops for consumption. This project presents a smart system for leaf blight detection that uses deep learning and convolutional neural networks (CNN) and is implemented on an Android platform. The system comprises an image acquisition module, a pre-processing module, and a leaf blight classification module. The image acquisition module captures images of leaves from a mobile camera, while the pre-processing module enhances the quality of images by removing noise and enhancing contrast. The leaf blight classification module uses a pre-trained CNN model to accurately classify images into healthy or diseased categories and provide the Confidence level of the Blight (disease) present.