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

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.