🤖 AI Summary
This study addresses the inefficiency of smallholder farmers in Bangladesh relying on manual identification of diseases in chili, potato, and tomato crops by proposing an ensemble deep learning framework based on ResNet50, EfficientNet-B0, and DenseNet121. Leveraging transfer learning, the approach freezes ImageNet-pretrained backbones and fine-tunes only the classification heads for ten epochs. Model outputs are integrated via simple averaging of softmax probabilities. Evaluated on the PlantVillage dataset, the method achieves an overall accuracy of 99.23%—with perfect classification (100%) for chili and potato diseases and 99.01% for tomato—while maintaining a real-time inference speed of 53 FPS on an NVIDIA T4 GPU. The work demonstrates that model diversity is crucial for performance gains and reveals that over-reliance on the best-performing individual model can degrade ensemble effectiveness.
📝 Abstract
Plant disease detection is still largely manual in Bangladesh, where extension workers eyeball leaf samples across millions of smallholdings. We built AgriMind to automate this: an ensemble of ResNet50, EfficientNet-B0, and DenseNet121 trained on 20,638 PlantVillage images across 15 pepper, potato, and tomato disease classes. Transfer learning with frozen ImageNet backbones and 10 epochs of head-only training keeps the pipeline lightweight. Individual models hit 96--97% on the held-out test set, but averaging their softmax outputs pushes the ensemble to 99.23% -- a two-thirds cut in error rate. We tried biasing the average toward the best validation model; it backfired. Dropping any single model also hurt. Pepper and potato classify perfectly; tomato, with ten visually similar classes, still reaches 99.01%. On an NVIDIA T4 GPU the full ensemble runs at 53 FPS. Whether that translates to real-time mobile use depends on TensorFlow Lite optimization -- work we have not yet completed.