Name
Abstract | ||
|---|---|---|
Traditional image processing has many problems in crop disease recognition, such as complicated and inefficient manual design. This paper studies the performance of deep learning algorithms in crop diseases. The research mainly optimizes the convolutional neural network from two aspects, one is the model structure, and the other is training label optimization. The basic convolutional neural network uses ResNet-50, InceptionV3, MobileNetV2, etc. Model training uses transfer learning to compare experimental data based on the performance of each model. the Top-1 accuracy rate of ResNet-B reached 91.2%, and the Top-5 accuracy rate was as high as 99.8%. Top1 accuracy of other models is increased by at least 0.7%
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3371425.3371497 | Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing |
Keywords | Field | DocType |
convolutional neural network, crop disease, image recognition, model optimization | Experimental data,Convolutional neural network,Crop disease,Computer science,Transfer of learning,Image processing,Artificial intelligence,Deep learning,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-7633-4 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Long | 1 | 0 | 0.34 |
| Changhua Liu | 2 | 0 | 0.68 |