High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.

Abstract

With the rapid development of artificial intelligence and deep learning technologies, their applications in the field of agriculture, particularly in plant disease detection, have become increasingly extensive. This study focuses on the high-precision detection of tomato diseases, which is of paramount importance for agricultural economic benefits and food safety. To achieve this aim, a tomato disease image dataset was first constructed, and a NanoSegmenter model based on the Transformer structure was proposed. Additionally, lightweight technologies, such as the inverted bottleneck technique, quantization, and sparse attention mechanism, were introduced to optimize the model's performance and computational efficiency. The experimental results demonstrated excellent performance of the model in tomato disease detection tasks, achieving a precision of 0.98, a recall of 0.97, and an mIoU of 0.95, while the computational efficiency reached an inference speed of 37 FPS. In summary, this study provides an effective solution for high-precision detection of tomato diseases and offers insights and references for future research.

Plant phenotyping relevance

トマト病害画像データセットを構築し、病徴を画像セグメンテーションで検出・定量するモデルを開発・評価しており、植物状態の取得方法が中心的です。

Code and data availability

The supplied blocks describe a self-collected, pixel-level annotated tomato disease image dataset (15,383 images) and the NanoSegmenter model, but no block contains a public deposit, availability statement, or authors' URL for the dataset, code, or trained model. The Kaggle wheat head dataset is a third-party resource,

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