Affiliation:
1. Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
2. Key Laboratory of Prevention and Control of Invasive Alien Species in Agriculture & Forestry of the North-Western Desert Oasis, Ministry of Agriculture and Rural Affairs, Urumqi 830052, China
Abstract
Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the performance and training speed of the model in CNNs. This study employed six training strategies, including Adam, SGD, Adam + StepLR, SGD + StepLR, Warm-up + Cosine annealing + SGD, Warm-up + Cosine, and annealing + Adam, with three initial learning rates (0.05, 0.01, and 0.001). Using the wheat stripe rust, wheat powdery mildew, and healthy wheat datasets, five lightweight CNN models, namely MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2, were evaluated. The results showed that upon combining the SGD + StepLR with the initial learning rate of 0.001, the MnasNet obtained the highest recognition accuracy of 98.65%. The accuracy increased by 1.1% as compared to that obtained with the training strategy with a fixed learning rate, and the size of the parameters was only 19.09 M. The above results indicated that the MnasNet was appropriate for porting to the mobile terminal and efficient for automatically identifying wheat leaf diseases.
Funder
National Natural Science Foundation of China
Crop Science Key Discipline Development Fund of Xinjiang Agricultural University
Subject
Paleontology,Space and Planetary Science,General Biochemistry, Genetics and Molecular Biology,Ecology, Evolution, Behavior and Systematics
Reference52 articles.
1. Curtis, B.C., Rajaram, S., and Gómez Macpherson, H. (2002). Bread Wheat: Improvement and Production, FAO.
2. Ruan, C., Dong, Y., Huang, W., Huang, L., Ye, H., Ma, H., Guo, A., and Sun, R. (2022). Integrating Remote Sensing and Meteorological Data to Predict Wheat Stripe Rust. Remote Sens., 14.
3. Mechanisms of powdery mildew resistance of wheat—A review of molecular breeding;Kang;Plant Pathol.,2020
4. Fusarium head blight of wheat: Pathogenesis and control strategies;Dweba;Crop Prot.,2017
5. A method for counting and classifying aphids using computer vision;Lins;Comput. Electron. Agric.,2020
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