Title: MS-ConvNeXt: a deep-learning method for tomato leaf diseases identification

Authors: Yunchao Li

Addresses: Jilin Engineering Vocational College, Siping 136001, Jilin, China

Abstract: Existing deep learning methods for tomato leaf disease identification are challenged by the multi-scale disease regions and complex backgrounds in tomato leaf images. A network for tomato leaf disease is proposed. In the proposed network, a cross-channel-and-spatial attention mechanism is first introduced in the ConvNeXt block (called A-ConvNeXt block) to avoid interference of invalid features from the complex backgrounds. Then, a multiscale feature mechanism is integrated into the backbone constructed by the A-ConvNeXt block to extract features across multiscale diseases. The fine multiscale and silence features are extracted to address the limitations on tomato leaf diseases. Experimental results on laboratory and natural datasets show that the identification accuracy reached 95.67%, which outperformed many other existing networks in comparison experiments. The proposed network may effectively improve tomato leaf disease identification and provide decision-making information for practical applications in modern agriculture.

Keywords: tomato leaf disease identification; attention mechanism; multiscale feature mechanism; deep learning.

DOI: 10.1504/IJIIDS.2026.155285

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.3/4, pp.273 - 288

Received: 21 Sep 2023
Accepted: 18 May 2024
Published online: 30 Jul 2026 *

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