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AgroXAI-MTNet: An Explainable Attention-Guided Multitask Deep Learning Framework for Crop Disease Classification, Lesion Segmentation, and Severity Estimation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 145 |
| Year of Publication: 2026 |
| Authors: Manuel Martin Ela Ndong, Sharmila Arun Chopade |
10.5120/ijcac97998ffa7bb
|
Manuel Martin Ela Ndong, Sharmila Arun Chopade . AgroXAI-MTNet: An Explainable Attention-Guided Multitask Deep Learning Framework for Crop Disease Classification, Lesion Segmentation, and Severity Estimation. International Journal of Computer Applications. 187, 145 ( Sep 2026), 1-8. DOI=10.5120/ijcac97998ffa7bb
Abstract
Evaluation of the AgroXAI-MTNet framework is performed using the PlantSeg dataset, which comprises 11,400 images of diseased plants with pixel-level lesion annotations and 8,000 images of healthy plants categorized according to plant type. The ground truth for disease severity is determined by calculating the percentage of diseased pixels relative to the total leaf area. Experimental evaluation is carried out in terms of accuracy, precision, recall, F1- score, and area under the receiver operating characteristic curve for disease classification; Dice coefficient and intersection over union for lesion segmentation; and mean absolute error and root mean square error for disease-severity estimation. The expected classification accuracy, precision, recall, and F1-score of the proposed framework are 94.80%, 94.20%, 93.90%, and 94.05%, respectively. The expected Dice coefficient and intersection over union for lesion segmentation are 0.8720 and 0.7790, respectively, while the target mean absolute error for disease-severity estimation is 3.85%.
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