Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model

نویسندگان

چکیده

Corn is one of the main food crops in China, and its area ranks top three world. However, corn leaf disease has seriously affected yield quality corn. To quickly accurately identify diseases, taking timely effective treatment to reduce loss yield. We proposed identifying diseases using Mobilenetv3 (CD-Mobilenetv3) model. Based on model, we replaced model’s cross-entropy function with a bias improve accuracy. Replaced squeeze excitation (SE) module efficient channel attention (ECA) parameters. Introduced cross-layer connections between Mobile modules utilize features synthetically. Then dilated convolutions model increase receptive field. integrated hybrid open-source dataset (CLDD). The test results CLDD showed accuracy reached 98.23%, precision 98.26%, recall F1 score 98.26%. are improved compared classic deep learning (DL) models ResNet50, ResNet101, ShuffleNet_x2, VGG16, SqueezeNet, InceptionNetv3, etc. value was 0.0285, parameters were lower than most contrasting models. experimental verified validity CD-Mobilenetv3 identification diseases. It provides adequate technical support for control

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ژورنال

عنوان ژورنال: Agronomy

سال: 2023

ISSN: ['2156-3276', '0065-4663']

DOI: https://doi.org/10.3390/agronomy13020300