Plant Disease detection using Image processing Techniques

Abstract

Plant disease detection is a critical task in modern agriculture, as early identification of plant
infections can significantly improve crop yield and quality. Traditional methods of disease
detection rely heavily on manual inspection by experts, which is time-consuming, laborintensive,
and often prone to human error. With the advancement of digital imaging and
computational techniques, image processing has emerged as an efficient and reliable solution
for automated plant disease diagnosis.
This project focuses on the development of a plant disease detection system using image
processing techniques. The system captures images of plant leaves and processes them to
identify symptoms such as spots, discoloration, and texture variations. Various preprocessing
steps, including image resizing, noise removal, and color space conversion, are applied to
enhance the quality of the input images. Segmentation techniques are then used to isolate the
infected regions from the healthy portions of the leaf.