# Lightweight deep learning for tomato disease detection: trends, challenges, and edge AI perspectives

## TL;DR

This paper reviews lightweight deep learning models for detecting tomato diseases and proposes an AI-powered framework for eco-friendly disease management in agriculture.

## Contribution

A novel AI-powered framework combining disease diagnosis with microbial biocontrol recommendations is proposed.

## Key findings

- MobileNetV2 and EfficientNetB0 achieved 99.9% accuracy in detecting tomato diseases.
- Lightweight models like MobileNetV2 and EfficientNetB0 are suitable for edge AI deployment in precision agriculture.
- A unique framework integrates AI diagnosis with region-specific microbial biocontrol strategies.

## Abstract

Tomato (Solanum lycopersicum) is a globally cultivated horticultural crop, yet its productivity is severely constrained by foliar and insect-vectored diseases that reduce its quality and production. Early and accurate diagnosis of these diseases, along with sustainable biocontrol strategies, is essential for improving crop health and reducing economic losses. This review synthesizes and evaluates the recent progress in lightweight deep learning models and edge AI for tomato disease detection, highlighting their potential for practical deployment in precision agriculture. A comprehensive survey of recent literature was conducted, which covers convolutional neural networks, transformer-based models, optimization techniques including pruning, quantization, and knowledge distillation, and use of explainable AI tools to enhance transparency and trust. In addition, experimental validation was performed by utilizing MobileNetV2 and EfficientNetB0 on a subset of tomato diseases that are most common and prevalent in Tamil Nadu. The test performance of both the models resulted in an overall accuracy of 99.9% and macro-F1 nearly 0.99. Further, a unique framework that combines AI-powered diagnosis with microbial biocontrol recommendations is proposed offering a solution to manage diseases in both eco-friendly and region-specific way. Overall, this work provides a roadmap for combining sustainable methods with AI-driven diagnosis, promoting resilient, scalable, and farmer-friendly agricultural systems.