Design-optimized YOLO11 classification via strategic CBAM attention injection and Grad-CAM explainability for reliable plant disease diagnosis

Al.koaerji Ali M. MozanM. Mozan, Ogorodnikova O.M., Ogorodnikov A.I.
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Reliable plant disease diagnosis requires not only high classification accuracy, but also stable generalization and interpretable decision-making. Although attention mechanisms are useful to these deep learning models, the performance also depends on where and how they are integrated into the network architecture. This study presents a design-optimized YOLO11m-based classification framework that systematically investigates the impact of Convolutional Block Attention Module (CBAM) injection at different architectural levels for plant disease diagnosis. We perform a comparative modelcontrolled analysis of three model architectures: (i) the baseline YOLO11m-Cls architecture lacking attention, (ii) only adding the backbone block along with CBAM and (iii) a hybrid architecture that includes reduced backbone attention combined with CBAM added at classification head. All models are trained and tested under the same experimental settings using a largescale dataset with around 90 000 images of 38 types of plant diseases. Experimental results clearly show that the uniform injection of CBAM into the backbone reduces stability but causes generalization to worsen with a higher validation loss and significantly lower Top-1 accuracy (≈ 90.5%), while the hybrid attention design balances stability and discrimination, with Top-1 accuracy up to 99.71%, Top-5 accuracy up to 99.99% and near-baseline validation behavior respectively Grad-CAMbased interpretability analysis also demonstrates that the hybrid model generates enhanced and biologically interpretable activation maps, which are more disease-specific with less distraction from background. Notably, the aim of our work is not for achieving superior performance over all classifiers but instead only to provide design-level evidence on how placing attention modulates rigidity and interpretability in YOLO-based classification models. The results provide practical architectural considerations for building dependable and interpretable AI systems in agriculture.

Keywords: YOLO11-based classification, attention placement strategy, convolutional block attention module (CBAM), explainable artificial intelligence (XAI), Grad-CAM visualization, plant disease diagnosis
Citation in English: Al.koaerji Ali M. MozanM. Mozan, Ogorodnikova O.M., Ogorodnikov A.I. Design-optimized YOLO11 classification via strategic CBAM attention injection and Grad-CAM explainability for reliable plant disease diagnosis // Computer Research and Modeling, 2026, vol. 18, no. 4, pp. 871-889
Citation in English: Al.koaerji Ali M. MozanM. Mozan, Ogorodnikova O.M., Ogorodnikov A.I. Design-optimized YOLO11 classification via strategic CBAM attention injection and Grad-CAM explainability for reliable plant disease diagnosis // Computer Research and Modeling, 2026, vol. 18, no. 4, pp. 871-889
DOI: 10.20537/2076-7633-2026-18-4-871-889
URL: http://crm-en.ics.org.ru/journal/article/3750/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Al.koaerji Ali M. MozanM. Mozan, Ogorodnikova O.M., Ogorodnikov A.I.

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