Chengling Zhou
ABSTRACT
Traditional conventional methods for identifying pests and diseases often rely depended on manual experience, making it difficult to ensure accuracy and low efficiency. Deep learning technology Learning technology deeply can automatically extract useful features from a large amount of image data, classify and recognize them, and improve enhance accuracy and efficiency This approach can help assist farmers detect pests and diseases in a timely manner, thus taking timely prevention and control measures to reduce the spread and loss of pests and diseases taking immediate steps to minimize its diffusion and loss as much as possible. This article in this paper, designs a deep learning based crop pest identification system designing a crop pest and disease recognition system which is based on deep learning, aiming to improve the accuracy and efficiency of crop pest identification. The system uses utilizes three classic convolutional neural networks (CNNs) as training models to achieve recognition of various common crop pest pests and disease types. After system testing and analysis, the results show demonstrate that the system has significant advantages in accuracy, real-time performance, and can meet fulfil practical application requirements. Future research directions Future studies can further optimize models, expand datasets, reduce false fake positives, and provide more accurate and efficient pest identification support for agricultural production.
- Liang Wanjie, Cao Jing, Sun Chuanliang, 2023. Research and development of crop disease recognition system based on deep learning. Chinese Journal of Agricultural Mechanization and Chemistry, 44 (09): 169-175. DOI:10.13733/j.jcam.issn.2095-5553.2023.09.024Google Scholar
Cross Ref
- Wang Zejun. 2023. Research on Classification and Detection Methods of Crop Pests Based on Convolutional Neural Networks. Qilu University of Technology. DOI:10.27278/d.cnki.gsdqc.2023.000124Google ScholarCross Ref
- Li Zihan, Zhou Shengbang, Zhao Ge, 2023. Overview of research on agricultural pest recognition based on convolutional neural networks. Jiangsu Agricultural Sciences, 51 (07): 15-23. DOI:10.15889/j.issn.1002-1302.2023.07.003Google ScholarCross Ref
- Huang Song. 2022. Research on the Recognition Technology of Corn Leaf Diseases and Pests Based on Deep Learning in Mobile Devices. Huazhong Agricultural University. DOI:10.27158/d.cnki.ghznu.2022.000608Google ScholarCross Ref
- LeCun, Y., Bengio, Y., & Hinton, G. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.Google ScholarCross Ref
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097-1105.Google Scholar
- Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.Google Scholar
- He, K., Zhang, X., Ren, S., & Sun, J. 2015. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385.Google Scholar
- Szegedy, C., 2015. Rethinking the Inception Architecture for Computer Vision. arXiv preprint arXiv:1512.00567.Google Scholar
Recommendations
Crop pest classification based on deep convolutional neural network and transfer learning
Graphical abstractDisplay Omitted
Highlights- A deep CNN model was developed for the classification of crop field insects.
- ...
AbstractThe growth of most important field crops such as rice, wheat, maize, soybean, and sugarcane are affected due to attack of various pests and the crop production is reduced due to different types of insects. The classification and ...
VGG-ICNN: A Lightweight CNN model for crop disease identification
AbstractCrop diseases cause a substantial loss in the quantum and quality of agricultural production. Regular monitoring may help in early stage disease detection an d thereby reduction in crop loss. An automatic plant disease identification system based ...
Survey on crop pest detection using deep learning and machine learning approaches
AbstractThe most important elements in the realm of commercial food standards are effective pest management and control. Crop pests can make a huge impact on crop quality and productivity. It is critical to seek and develop new tools to diagnose the pest ...
Comments