Xiuli Si1, Biao Hong1, Yuanhui Hu1, Lidong Chu2,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829
Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA) module. The module follows the… More >
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