Cucumber Leaves Diseases Detection through Computational Approaches: A Review
R. Manavalan *
Computer Science, Arignar Anna Government Arts College, Villupuram, Affiliated to Thiruvalluvar University, Vellore, Tamilnadu, India.
*Author to whom correspondence should be addressed.
Abstract
Agriculture is the largest economic sector since it generates both food and raw materials for industry. Plants are a primary source of food, energy, vitamins, and minerals. Cucumber is a widely produced food crop worldwide. Cucumber plants grow naturally in both temperate and tropical environments. Many diseases and pests can harm cucumbers at any stage of development, reducing their growth and causing major economic losses in agriculture. Anthracnose, target leaf spot, sticky stem blight and angular leaf spot are bacterial diseases that can cause serious damage to plant leaves. Thus, early detection and treatment of cucumber diseases are critical for yield enhancement. The diagnosis of cucumber leaf diseases by naked eyes results in improper pesticide measurement. To maximize productivity, early detection of cucumber illnesses is required. This leads to inaccuracy in pesticide dosage control. It is necessary to automate disease detection and diagnosis in order to raise cucumber production and quality. These algorithms extract features from cucumber plant photos and identify diseases early on. An overview of image processing and machine learning techniques used to detect disease in cucumber leaves. This paper also discusses the future potential for computer algorithms for evaluating cucumber leaf images.
Keywords: Cucumber, plant diseases, computational methods, SVM, neural network