The Detection and Grading of Grape Leaf Disease Using Fuzzy Logic and Artificial Neural Network

Abstract

Research in agriculture focusing on the automatic detection of grape leaf diseases is crucial, as it can aid in overseeing extensive grape fields and promptly identifying disease symptoms on leaves. In the agricultural industry, crops often suffer damage due to diseases. Early diagnosis of these diseases allows for the protection of crops through various treatments. The cultivation of grape plants has rapidly advanced in both quality and quantity, yet pests and diseases, particularly on leaves, have impeded the quality of agricultural products. Suppose pest infestations on grape crops and leaves are not well monitored and addressed on time. In that case, the quality and yield of grape farming will decline, leading to increased poverty, food insecurity, and mortality rates. This significant impact can disrupt the economy of any nation, especially those where about 75% of the population relies on agricultural products for their livelihood and survival. A primary challenge for farmers is reducing or eliminating pest growth that affects crop yields. Pests are organisms that spread disease, cause damage, or are bothersome. Pests that frequently harm plants include aphids, fungi, beetles, thrips, flies, snails, slugs, caterpillars, and mites. These pests can cause fitful disease dawn, resulting in famine and food shortages.