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Article Abstract
Timely disease detection and pest treatment are key issues in modern agricultural production, especially in large-scale crop agriculture. However, it is very time and effort-consuming to identify plant diseases manually. This paper proposes a deep learning model for agricultural crop disease identification based on AlexNet and Inception-V4. AlexNet and Inception-V4 are combined and modified to achieve an efficient but good performance. Experimental results on the expanded PlantVillage dataset show that the proposed model outperforms the compared methods: AlexNet, VGG11, Zenit, and VGG16, in terms of accuracy and 1 scores. The proposed model obtains the highest accuracy for corn, tomato, grape, and apple: 94.5%, 94.8%, 92.3%, and 96.5%, respectively. Also, the highest 1 scores for corn, tomato, grape, and apple: 0.938, 0.910, 0.945, and 0.924, respectively, are obtained. The results indicate that the proposed method has promising generalization ability in crop disease identification.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482484 | PMC |
| http://dx.doi.org/10.1155/2022/5862600 | DOI Listing |
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