Image Recognition of Citrus Diseases Based on Deep Learning
Source abstract
Abstract
In recent years, with the development of machine learning and deep learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition method of citrus diseases based on deep learning is proposed. We built a citrus image dataset including six common citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed, model size, accuracy. Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy. Finally, we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal, and put forward relevant suggestions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (24)
Show source-provided references
1. Advances in image recognition technology of crop diseases and insect pests based on deep learning
2. Application of image recognition processing technology in agricultural engineering
3. The application of deep learning in agriculture in China computer engineering and applications
4. Image augmentation-based food recognition with convolutional neural networks10.32604/cmc.2019.04097 ↗
5. Identification of crop diseases based on convolution neural network
6. Effects of postharvest oligochitosan treatment on anthracnose disease in citrus fruit10.1007/s00217-014-2385-7 ↗
7. Genome editing of the disease susceptibility gene CsLOB1 in citrus confers resistance to citrus canker10.1111/pbi.12677 ↗
8. Two fungal diseases of citrus worthy of attention
9. Sexual reproduction in the citrus black spot pathogen10.1094/PHYTO-11-16-0419-R ↗
10. ImageNet classification with deep convolutional neural networks
11. Coverless real-time image information hiding based on image block matching and dense convolutional network10.1007/s11554-019-00917-3 ↗
12. An encrypted image retrieval method based on harris corner optimization and LSH in cloud computing10.1109/ACCESS.2019.2894673 ↗
13. Captcha recognition based on deep convolutional neural network10.3934/mbe.2019292 ↗
14. A robust watermarking scheme in YCbCr color space based on channel coding10.1109/ACCESS.2019.2896304 ↗
15. A biological image classification method based on improved CNN10.1016/j.ecoinf.2020.101093 ↗
16. Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping10.1016/j.knosys.2019.105375 ↗
17. Deep learning in agriculture: A survey10.1016/j.compag.2018.02.016 ↗
18. Advances in crop disease image recognition based on deep learning
19. Deep learning models for plant disease detection and diagnosis10.1016/j.compag.2018.01.009 ↗
20. Using deep learning for image based plant disease detection10.3389/fpls.2016.01419 ↗
21. MobileNetV2: Inverted residuals and linear bottlenecks
22. The effectiveness of data augmentation in image classification using deep learning
23. A smart mobile diagnosis system for citrus diseases based on densely connected convolutional networks10.1109/ACCESS.2019.2924973 ↗
24. Intelligent pest identification system based on deep learning