Technological Innovation in Precision Agriculture: An EfficientNet-B0 Transfer Learning Framework for Grape Leaf Disease Diagnosis

This paper advances mobile precision agriculture by engineering an optimized EfficientNet-B0 transfer learning framework that resolves the critical trade-off between high diagnostic precision and edge-compute efficiency in vineyard biosecurity. Unlike legacy architectures like VGG16 that suffer from high parameter redundancy (138M+ parameters) and slow inference latency, our compound-scaled model achieves a peak 94.80% diagnostic accuracy and 94.80% F1-score across field-collected grape foliage classes while maintaining a remarkably lightweight profile of 5.3 million parameters, a 21 MB memory footprint, and an execution cost of just 0.39 GFLOPs. Furthermore, by evaluating performance directly on outdoor, variable-field imagery rather than controlled laboratory benchmarks, this work demonstrates a practical, parameter-efficient pipeline tailored for real-time, offline edge-AI deployment on resource-constrained mobile hardware and autonomous agricultural drones.