Efficient Deep Learning Ensemble of Lightweight CNNs and Vision Transformers for Real-Time Plant Disease Diagnosis - Mruna Dubey; Aruna Sri P.S.G.; Suresh Kumar Jha; Nupur Nupur; Girish Bhiogade; Neeraj Kumar (Malikussaleh University, Aceh, Indonesia, 2025)
Timely identification of plant diseases plays a vital role in protecting crop yield and supporting effective decision-making in precision agriculture. Conventional computer vision models achieve high recognition accuracy but often require substantial computing power, making them impractical for low-cost edge hardware widely used in rural areas. In this work, a compact deep learning ensemble is presented, combining three lightweight convolutional neural networks—MobileNetV3-Small, EfficientNet-B0, and ShuffleNetV2—with a Vision Transformer (ViT-B/16). The models operate in parallel, and their outputs are merged using a weighted late-fusion approach, with fusion weights determined through systematic grid search to achieve the best trade-off between predictive performance and processing speed. The Plant Village dataset, consisting of 54,303 images from 38 healthy and diseased leaf categories, was used for evaluation. To improve robustness, the training data were augmented through geometric transformations, contrast adjustment, and controlled noise addition. When tested on a Raspberry Pi 4 device, the ensemble reached an accuracy of 97.85%, precision of 97.67%, recall of 97.92%, and F1-score of 97.79%, with an average inference time of 20.5 ms and a total size of 14.6 MB. These results surpassed those of all individual models and conventional machine-learning baselines. Statistical testing using McNemar’s method confirmed the significance of the improvement (p 0.05). Precision–Recall analysis indicated strong resistance to false positives, while accuracy–latency assessment confirmed suitability for real-time field operation. The proposed system offers a practical, resource-efficient framework for on-site plant disease diagnosis in areas with limited connectivity and computing resources. Further development will focus on adaptation to field-captured imagery, hardware-aware model compression, and the integration of additional sensing modalities such as hyperspectral and thermal imaging.