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Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning
| Content Provider | MDPI |
|---|---|
| Author | Sun, Yunyun Liu, Yutong Zhou, Haocheng Hu, Huijuan |
| Copyright Year | 2021 |
| Description | Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning optimizer for plant diseases identification. To examine the recognition and generalization capability of the DM optimizer, we discuss the hyper-parameter tuning and convolutional neural networks models across the plantvillage dataset. We further conduct comparison experiments on popular non-adaptive learning rate methods. The proposed approach achieves an average validation accuracy of no less than 97% for plant diseases prediction on several state-of-the-art deep learning models and holds a low sensitivity to hyper-parameter settings. Experimental results demonstrate that the DM method can bring a higher identification performance, while still maintaining a competitive performance over other non-adaptive learning rate methods in terms of both training speed and generalization. |
| Starting Page | 9468 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11209468 |
| Journal | Applied Sciences |
| Issue Number | 20 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-12 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Computer Science Convolutional Neural Networks Non-adaptive Optimization Hyper-parameter Crop Identification |
| Content Type | Text |
| Resource Type | Article |
