Early detection and control of anthracnose disease in cashew leaves to improve crop yield using image processing and machine learning techniques

Abstract

Agriculture is one of the primary pillars powering India's economy. It is alarming to note that India's agriculture rate is declining steeply. Climate change, environmental pollution, and soil erosion are well-known factors affecting crop productivity. The increasing prevalence of plant diseases is also a signi cant contributing factor affecting agriculture.Early disease detection and mitigation actions based on identi ed diseases in the plants are critical in increasing crop productivity. This study considers a machine-learning model for detecting disease in cashew leaves. This work concentrates on Anthracnose disease, which leads to severe yield loss when it affects the cashew plant. In this regard, cashew leaves are collected and used to train various machine learning classi ers to identify and classify the disease. This work focuses on the segmentation and classi cation of leaves using various Machine Learning models. For this,

ELDA: enhanced linear discriminant analysis for cashew crop disease detection using precision agriculture

“…It can also cluster/group irregularly shaped patches in an image into a single cluster. The K-means segmentation approach is applied because it performed better than other segmentation methods on the CCDDB dataset 20 …”

“…The K-means segmentation approach is applied because it performed better than other segmentation methods on the CCDDB dataset. 20 K-mean segmentation: to find an object's boundaries. Consider cd as the centroid value and d as distance.…”