Title: Leveraging deep learning for strategic decision-making in sustainable agriculture: enhancing plant disease detection for optimised supply chain management and ecosystem health

Authors: Chandrakant Mallick; Anita Patra; Shreela Dash; Pradipta Kumar Mishra; Bijay Kumar Paikaray

Addresses: Department of Computer Science and Engineering, Gandhi Institute for Technological Advancement (GITA) Autonomous College, Bhubaneswar, Odisha, India ' School of Management, Centurion University of Technology and Management, Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, Silicon University, Bhubaneswar, Odisha, India ' Faculty of Engineering & Technology, Sri Sri University, Cuttack, Odisha, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India

Abstract: Agriculture, an essential component of our society, suffers the continuous threat of plant diseases that can potentially destroy crops and threaten the country's food security. Traditional methods of disease detection that rely on human visual inspection are often unreliable, particularly when it comes to recognising early signs of diseases. This research proposes an innovative method that integrates image processing and deep learning techniques to transform the field of crop plant disease detection in agriculture. Our process utilises computer vision and Convolutional Neural Network (CNN) to detect various crop plant diseases, including viral outbreaks and fungal infections, in different plant species and environmental situations. Our solution combines advanced technologies, and an exhaustive evaluation procedure to provide farmers with accurate and fast information. This enables them to reduce crop losses and improve agricultural sustainability.

Keywords: plant disease detection; image processing; deep learning; convolutional neural networks; computer vision; agricultural crops.

DOI: 10.1504/IJAMS.2026.151290

International Journal of Applied Management Science, 2026 Vol.18 No.1, pp.90 - 110

Received: 14 May 2024
Accepted: 11 Oct 2024
Published online: 22 Jan 2026 *

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