Leveraging Deep Learning for Early Detection and Diagnosis of Wheat Diseases: Challenges and Innovations

Chemesse Ennehar BencherietHala HamouchiMohamed Islem Hadri

DOI 10.7160/aol.2025.170403
No 4/2025, December
pp. 27-36

Bencheriet, C. E., Hamouchi, H. and Hadri, M. I. (2025) "Leveraging Deep Learning for Early Detection and Diagnosis of Wheat Diseases: Challenges and Innovations", AGRIS on-line Papers in Economics and Informatics, Vol. 17, No. 4, pp. 27-36. ISSN 1804-1930. DOI 10.7160/aol.2025.170403.

Abstract

This research introduces a deep learning system for the early identification and categorization of wheat illnesses, with the objective of optimizing crop health and promoting agricultural sustainability. Results in up to high classification accuracy for brown rust, yellow rust, leaf rust, and septoria. The combination of artificial intelligence (AI) with image processing methodologies such as rescaling and augmentation allows the system to accurately classify wheat crops that are well or unhealthy. The presented system is of great interest for precision agriculture, providing an affordable means to reduce the application of pesticides and encourage sustainable agricultural practices. Ongoing research involves linking this diagnostic platform with drone technology to facilitate on-demand, point-by-point disease surveillance and monitoring across large areas, further extending the platform’s applicability in field applications for food securit.

Keywords

Wheat disease detection, deep learning, convolutional neural networks (CNNs).

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