Automated Plant Disease Detection and Recognition Using Convolutional Neural Network

Ayesha Rehan; Avanish Verma; Chitransh Nigam

doi:10.55524/ijircst.2026.14.2.7

Abstract

Sustainable Agriculture has reduced environmental harms, aids and expands natural resources for productive purpose. It allows the production of crops to ensures global food security. Plant Diseases leading to considerable crop losses and economic challenges for farmers. In the era of Modern technology there is a need of fast and accurate detection is essential for effective intervention and management. Modern innovation and research developments in machine learning and deep learning enabled automated plant disease detection that are providing fast scalable, and highly accurate solutions. While previous models are implemented for single type plant to get higher accuracy and therefore, they need high-resolution images. Convolutional Neural Network provide excellent performance in classifying and identifying diseases from datasets of plant leaves images with low resolutions. The usage of pre-trained models and a focus on the fine tuning of the hyperparameters leads the highest accuracy in detecting plant disease. However, there is need of strategy, adaptability, and robustness to achieve goals.

Keywords

Plant Disease Detection, Convolutional Neural Network, Accuracy

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Cites this article as

A. Rehan, A. Verma, C. Nigam, "Automated Plant Disease Detection and Recognition Using Convolutional Neural Network ", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-14, Issue-2, Page No-52-59, 2026. Available from: https://doi.org/10.55524/ijircst.2026.14.2.7

Corresponding Author

Ayesha Rehan

B.Tech Scholar , Department of Computer Science & Engineering, Khwaja Moinuddin Chishti Language University Lucknow, Barabanki, India

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