Mobile-Friendly Deep Learning for Plant Disease Detection: A Lightweight CNN Benchmark Across 101 Classes of 33 Crops

TL;DR

This paper presents a lightweight, mobile-friendly deep learning model for accurate detection of 101 plant diseases across 33 crops, demonstrating high accuracy and efficiency suitable for deployment on resource-limited devices.

Contribution

The study introduces a comprehensive dataset and benchmarks multiple lightweight CNN architectures, highlighting EfficientNet-B1 as the optimal model for mobile plant disease detection.

Findings

EfficientNet-B1 achieved 94.7% accuracy.

Lightweight models can effectively detect diverse plant diseases.

The proposed approach is suitable for real-world mobile deployment.

Abstract

Plant diseases are a major threat to food security globally. It is important to develop early detection systems which can accurately detect. The advancement in computer vision techniques has the potential to solve this challenge. We have developed a mobile-friendly solution which can accurately classify 101 plant diseases across 33 crops. We built a comprehensive dataset by combining different datasets, Plant Doc, PlantVillage, and PlantWild, all of which are for the same purpose. We evaluated performance across several lightweight architectures - MobileNetV2, MobileNetV3, MobileNetV3-Large, and EfficientNet-B0, B1 - specifically chosen for their efficiency on resource-constrained devices. The results were promising, with EfficientNet-B1 delivering our best performance at 94.7% classification accuracy. This architecture struck an optimal balance between accuracy and computational efficiency, making it well-suited for real-world deployment on mobile devices.