Managing The Tomato Leaf Disease Detection Accuracy Using Computer Vision Based Deep Neural Network - P, Dr. Sreelatha; Udayakumar, Mr. Sridhar; Karthick, Dr. S.; Chowdary Ch, Smitha; Kavya, K. Ch. Sri; M, Dr. Madiajagan (The journal of contemporary issues in business and government, 2021)
Development of leaf disease in the agricultural sector would decrease crop yield output. Thus, leaf disease identification can be achieved in an automatic way to increase the yield in the agriculture sector. However, most of the disease recognition system works with poor disease recognition due to varying patterns of leaf disease which impair detection accuracy. In this article, we are managing this issue by designing a computer vision model that assists in building a system that involves real-time image detection, feature extraction and image classification. The findings are given by the classifier, whether the leaf is diseased or not. In this paper we use Deep Neural Network (DNN) for real-time image classification. The experimental findings on tomato plant indicate that classification rates have increased with the proposed system relative to other current methods.