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- W4367153125 abstract "Maize is the most productive food crop in the world, and its leaves are extremely susceptible to disease. However, the yearly crop yields are reduced by the disease and its interference to corn leaf diseases. Therefore, finding a way to identify corn leaf diseases more quickly and accurately is a current concerning matter in agriculture. Traditional machine learning extracts the crop disease features such as texture, color and visual as the basis for machine learning model classification. Yet, many disease images are needed to train the machine learning algorithms. It is difficult to collect disease samples evenly and meticulously during production as well. With the progress of smart agriculture, in-depth learning, and the introduction of agriculture, especially in the field of crop disease diagnosis, CNN in-depth learning technology has made varying degrees of progress in identifying crop diseases and pests. The experimental results of many scholars have verified that various CNN models are very effective in identifying leaf diseases. These improved models can better extract the characteristics of the target and improve the accuracy of identifying corn leaf diseases." @default.
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- W4367153125 date "2023-01-01" @default.
- W4367153125 modified "2023-10-17" @default.
- W4367153125 title "Identification and Analysis of Maize Leaf Diseases and Insect Pests Based on Machine Learning" @default.
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- W4367153125 doi "https://doi.org/10.1007/978-981-19-8406-8_24" @default.
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