Background Bias Assessment and Mitigation in Plant Disease Detection Using EfficientNet-B0 with Image Processing

arXiv:2611.00049v1 Announce Type: cross Abstract: Plant disease detection using deep learning has achieved remarkable accuracies exceeding 99% on benchmark datasets. However, these metrics may mask a critical vulnerability: models may be taking advantage of false background correlations instead of learning real pathological features. This study presents a systematic investigation of background bias in plant disease detection through a two-stage training framework with quantitative validation. We propose a three-condition evaluation protocol testing models on original, leaf-only, and background-only images to directly measure background dependency. Our approach combines standard transfer learning using EfficientNet-B0 and manipulation of background information during optimization. Experimental results on the PlantVillage dataset (20,638 images, 15 disease classes) show that while our model achieves 98.97% accuracy on the original images and maintains 96.62% accuracy on leaf-only images, the performance drops to 23.58% on background-only images. The calculated background bias (BBC) coefficient of 0.762 indicates that a large part of the model's predictive performance depends on leaf-related features rather than background distortions. This research offers clear evidence that even with the most advanced models, environmental shortcuts can be leveraged, and this highlights the importance of bias evaluation. The approach that has been used has laid a foundation for the development of agricultural AI that is ready for deployment. The implementation, models, and evaluation process are all publicly available.