A lightweight convolutional neural network for tea leaf disease and pest recognition

Abstract

The tea industry plays a vital role in China's green economy. Tea trees (Melaleuca alternifolia) are susceptible to numerous diseases and pest threats, making timely pathogen detection and precise pest identification critical requirements for agricultural productivity. Current diagnostic limitations primarily arise from data scarcity and insufficient discriminative feature representation in existing datasets. This study presents a new tea disease and pest dataset (TDPD, 23-class taxonomy). Five lightweight convolutional neural networks (LCNNs) were systematically evaluated through two optimizers, three learning rate configurations and six distinct scheduling strategies. Additionally, an enhanced MnasNet variant was developed through the integration of SimAM attention mechanisms, which improved feature discriminability and increased the accuracy of tea leaf disease and pest classification. Model validation employs both our proprietary TDPD dataset and an open-access dataset, with performance evaluation metrics including average accuracy, F1 score, recall, and parameter size. The experimental results demonstrated the superior classification performance of the model, which achieved accuracies of 98.03% based on TDPD and 84.58% based on the public dataset. This research outlines an effective paradigm for automated tea disease and pest detection, with direct applications in precision agriculture through integration with UAV-mounted imaging systems and mobile diagnostic platforms. This study provides practical implementation pathways for intelligent tea plantation management.

Plant phenotyping relevance

茶葉画像から病害・害虫状態を推定するCNN、データセット構築、モデル比較・検証が研究の中心であり、植物の病害状態を対象とする実質的なフェノタイピング手法研究である。

Code and data availability

The paper introduces a proprietary TDPD image dataset and an MnasNet-SimAM model, but the Data availability statement explicitly says no datasets were generated or analysed, and no code, model, or dataset deposit URL is provided anywhere in the supplied blocks. The only public dataset mentioned is an unnamed 'open-acct

No evidence-backed public reproduction asset is currently recorded.