# AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification Source: [AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification]() Collection index: [llms.txt]() Data updated: 2026-05-19T05:00:13.117Z Authors: Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely Published: 2026-05-15T15:37:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on PlantVillage 15-class (test)](): Image Classification; PlantVillage 15-class (test); Test Accuracy: 99.23. ## Original sources and implementation [Original paper]() [PDF]()