Assessment of Yellow Rust (<i>Puccinia striiformis</i>) Infestations in Wheat Using UAV-Based RGB Imaging and Deep Learning

Assessment of Yellow Rust (<i>Puccinia striiformis</i>) Infestations in Wheat Using UAV-Based RGB Imaging and Deep Learning

Yellow rust (<i>Puccinia striiformis</i>) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Do...

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Bibliographic Details
Main Authors: Atanas Z. Atanasov, Boris I. Evstatiev, Asparuh I. Atanasov, Plamena D. Nikolova
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
yellow rust (<i>Puccinia striiformis</i>)
identification
pixel-based classification
deep learning
RGB imaging
UnetClassifier
Online Access:https://www.mdpi.com/2076-3417/15/15/8512
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Summary:Yellow rust (<i>Puccinia striiformis</i>) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach’s potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model’s application to other cereal diseases.
ISSN:2076-3417

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