Pattern Based Leaves Disease Classification Using AI

Madan Mohan Mishra. and Pramod Singh
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Madan Mohan Mishra.: Department of Computer Application & Information Technology and Science A K S University, Satna MP, India
Pramod Singh: Department of Computer Application & Information Technology and Science A K S University, Satna MP, India

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 6, 908-914

Abstract: Artificial Intelligence (AI) is an overarching domain that integrates a variety of techniques, tools, and systems designed to enable machines to learn from data and perform predictive or decision-making tasks. Within this domain, computer vision stands out as a pivotal subfield, offering substantial contributions across multiple sectors, including agriculture. The integration of AI and computer vision has given rise to smart farming an advanced form of agriculture where traditional cultivation practices are optimized through intelligent technologies to enhance productivity, precision, and resource efficiency. A prominent application of computer vision in agriculture is the detection and classification of plant diseases through image classification and object detection techniques. These methods facilitate the automated identification of infected plant leaves by analyzing visual indicators such as lesions, spots, and discolorations. In this context, a specialized approach termed the Single Sample Computer Vision Recognition Algorithm (SSCVRA) has been employed to detect disease symptoms in eggplant leaves. The model was trained and validated using a dataset comprising 20,000 images, achieving an impressive classification accuracy of 99.47%. SSCVRA conducts disease identification by extracting and matching relevant image features, focusing on such parameters of color, shape, texture and comparing with consecutive region area of the leaf. The algorithm is capable of distinguishing among multiple plant diseases, including Powdery Mildew, Bacterial Leaf Spot, and Early Blight. Visualization outputs further support diagnosis by providing quantitative metrics on image similarity and disease likelihood based on color-based image analysis.

Date: 2025
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