Automated Disease Severity Measurement Using Image Processing

Abstract

Apple orchards are vital to the agricultural industry, but they face constant threats from diseases like apple scab, powdery mildew, rust, and rot. Traditional methods for measuring disease severity in apple leaves are labour-intensive, subjective, and prone to errors, limiting their practicality for large-scale agriculture. This paper introduces an innovative approach leveraging image processing techniques to automate disease severity measurement in apple leaves, offering a transformative solution for orchard management. The research aims to provide an efficient and accurate method for assessing disease severity, addressing the critical need for precise disease management and improved crop yields. Current methods relying on visual inspections by human experts are inefficient and unreliable. In contrast, the proposed method automates the measurement process, saving time and resources, enabling frequent monitoring, and enhancing early disease detection. This automation, in turn, leads to more effective disease management and increased crop yield and quality. The methodology involves image preprocessing, grayscale conversion, histogram equalization, thresholding, and defect area extraction, ultimately resulting in a quantitative disease severity index. The study utilizes a diverse dataset of apple leaf images, allowing for robust model development capable of accurate disease detection under varying conditions. This research holds significant implications for disease management and crop yield optimization, with broader applicability across agriculture. It paves the way for precision agriculture by enabling data-driven decisions on disease control measures while reducing the environmental impact of chemical inputs. Furthermore, the image processing techniques developed here can be adapted to other crops and plant diseases, promising efficient and accurate disease severity measurement methods across agricultural domains.