# Cross-scale detection and cross-crop generalization verification of tomato diseases in complex agricultural environments

## TL;DR

This paper introduces ToMASD, a lightweight model for detecting tomato leaf diseases in complex environments and generalizing to other crops.

## Contribution

The novel ToMASD model combines multi-scale feature decoupling and adaptive alignment for high-precision disease detection and cross-crop generalization.

## Key findings

- ToMASD achieves 84.3% average precision, outperforming thirteen models by 4.7% to 12.1%.
- The model generalizes to common bean and potato with 92.7% average precision after transfer learning.
- False detection rates in foggy and strong light conditions are controlled at 6.3% and 9.8%, respectively.

## Abstract

In order to overcome the key challenges associated with detecting tomato leaf disease in complex agricultural environments, such as leaf occlusion, variation in lesion size and light interference, this study presents a lightweight detection model called ToMASD. This model integrates multi-scale feature decoupling and an adaptive alignment mechanism. The model innovatively comprises a dual-branch adaptive alignment module (TAAM) that achieves cross-scale lesion semantic alignment via a dynamic feature pyramid, a local context-aware gated unit (Faster-GLUDet) that uses a spatial attention mechanism to suppress background noise interference, and a multi-scale decoupling detection head (MDH) that balances the detection accuracy of small and diffuse lesions. On a dataset containing six types of disease under various weather conditions, ToMASD achieves an average precision of 84.3%,.by a margin of 4.7% to 12.1% over thirteen mainstream models. The computational load is compressed to 7.1 GFLOPs. Through the introduction of a transfer learning paradigm, the pre-trained weights of the tomato disease detection model can be transferred to common bean and potato detection tasks. Through domain adaptation layers and adversarial feature decoupling strategies, the domain shift problem is overcome, achieving an average precision of 92.7% on the target crop test set. False detection rates in foggy and strong light conditions are controlled at 6.3% and 9.8%, respectively. This study achieves dual breakthroughs in terms of both high-precision detection in complex scenarios and the cross-crop generalization ability of lightweight models. It provides a new paradigm for universal agricultural disease monitoring systems that can be deployed at the edge.