Deep Learning-Based Ordinal Severity Grading of Mustard Flea Beetle Infestation from Plant Images

Damage to mustard caused by feeding by flea beetles is an important constraint to mustard production, especially during early stand establishment when feeding may cause a reduction in plant vigor and development. However, in contrast to the detection of pests and the determination of overall damage, the gradation of severity of flea beetle infestation has not been thoroughly studied. This research proposes an automated grading system for the severity of a mustard flea beetle infestation using a deep learning approach and four ordered classes: healthy, mild, moderate and severe. The framework includes source aware data partitioning, redundancy control, imbalance-aware learning, ordinal severity modelling, and comparison of five CNN architectures. The accuracy, balanced accuracy, macro-F1, Quadratic Weighted Kappa (QWK) and ordinal mean absolute error (MAE) are used for performance evaluation. The highest individual accuracy is obtained by ConvNeXt-Tiny with 94.93%, the best balanced accuracy is 0.7341, the best macro-F1 is 0.6866, the best QWK is 0.9295 and the best ordinal MAE obtained is 0.0510. The performance is further enhanced to 95.26% accuracy, 0.6930 macro-F1 and 0.9351 QWK by using prediction-level ensemble averaging. The mean of the three-fold cross validation is 0.6296 ± 0.0028 (source grouped). The activation energy using Grad-CAM analysis is focused in the estimated leaf areas only 72.1%, whereas the mean leaf area index is 38.8%. The results indicate that deep learning has the potential to be used for automated multi-level flea beetle infestation grading and can be used as a starting point for further validation in different field conditions.