Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
Source abstract
Abstract
Peanut is an important food crop, and diseases of its leaves can directly reduce its yield and quality. In order to solve the problem of automatic identification of peanut-leaf diseases, this paper uses a traditional machine-learning method to ensemble the output of a deep learning model to identify diseases of peanut leaves. The identification of peanut-leaf diseases included healthy leaves, rust disease on a single leaf, leaf-spot disease on a single leaf, scorch disease on a single leaf, and both rust disease and scorch disease on a single leaf. Three types of data-augmentation methods were used: image flipping, rotation, and scaling. In this experiment, the deep-learning model had a higher accuracy than the traditional machine-learning methods. Moreover, the deep-learning model achieved better performance when using data augmentation and a stacking ensemble. After ensemble by logistic regression, the accuracy of residual network with 50 layers (ResNet50) was as high as 97.59%, and the F1 score of dense convolutional network with 121 layers (DenseNet121) was as high as 90.50. The deep-learning model used in this experiment had the greatest improvement in F1 score after the logistic regression ensemble. Deep-learning networks with deeper network layers like ResNet50 and DenseNet121 performed better in this experiment. This study can provide a reference for the identification of peanut-leaf diseases.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Peanut Plant Research Studies · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, China
Guangdong Laboratory for LingNan Modern Agriculture, Guangzhou 510642, China
Department of Electrical & Computer Engineering, College of Engineering, University of Washington, Seattle, WA 98195-2500, USA
Funding
Key-Area Research and Development Program of Guangdong Province · Characteristic innovation projects of Guangdong Provincial Department of education in 2019
References (33)
Show source-provided references
1. Present Situation and Development Strategies of Peanut Production, Breeding and Seed Industry in Guangdong
2. Late Leaf Spot Effects on Growth, Photosynthesis, and Yield in Peanut Cultivars of Differing Resistance10.2134/agronj2010.0322 ↗
3. Qi, H., Zhu, B., Kong, L., Yang, W., Zou, J., Lan, Y., and Zhang, L. (2020). Hyperspectral inversion model of chlorophyll content in peanut leaves. Appl. Sci., 10.10.3390/app10072259 ↗
4. Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection10.1007/s11633-019-1210-y ↗
5. Pham, B.T., Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, H.P.H., Phong, T.V., Nguyen, D.H., Le, H.V., and Mafi-Gholami, D. (2020). Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12.10.3390/sym12061022 ↗
6. Kranjčić, N., Medak, D., Župan, R., and Rezo, M. (2019). Machine learning methods for classification of the green infrastructure in city areas. ISPRS Int. J. Geo Inf., 8.10.3390/ijgi8100463 ↗
7. Technology, Performance evaluation of various statistical classifiers in detecting the diseased citrus leaves
8. Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning10.1155/2017/2917536 ↗
9. Method of Leaf Identification Based on Multi-feature Dimension Reduction
10. Plant leaf disease recognition based on kernel K-means clustering algorithm
11. Es-saady, Y., El Massi, I., El Yassa, M., Mammass, D., and Benazoun, A. (2016, January 4–7). Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers. Proceedings of the 2016 International Conference on Electrical and Information Technologies (ICEIT), Tangiers, Morocco.10.1109/EITech.2016.7519661 ↗
12. Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques10.1016/j.compag.2018.11.016 ↗
13. The method of recognition of damage by disease and insect based on laminae
14. Wang, H., Li, G., Ma, Z., and Li, X. (2012, January 29–31). Image recognition of plant diseases based on principal component analysis and neural networks. Proceedings of the 2012 8th International Conference on Natural Computation, Chongqing, China.10.1109/ICNC.2012.6234701 ↗
15. Huang, J., and Kingsbury, B. (2013, January 26–31). Audio-visual deep learning for noise robust speech recognition. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.10.1109/ICASSP.2013.6639140 ↗
16. Yu, D., and Deng, L. (2016). Automatic Speech Recognition, Springer.10.1007/978-1-4471-5779-3 ↗
17. Tao, J., Wang, H., Zhang, X., Li, X., and Yang, H. (2017, January 29–31). An object detection system based on YOLO in traffic scene. Proceedings of the 2017 6th International Conference on Computer Science and Network Technology (ICCSNT), Chongqing, China.10.1109/ICCSNT.2017.8343709 ↗
18. Zhu, Y., Fan, H., and Yuan, K. (2019). Facial expression recognition research based on deep learning. arXiv.
19. Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y. (2017, January 21–26). Realtime multi-person 2d pose estimation using part affinity fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.143 ↗
20. Toshev, A., and Szegedy, C. (2014, January 23–28). Deeppose: Human pose estimation via deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.214 ↗
21. Identification of plant leaf diseases using a nine-layer deep convolutional neural network10.1016/j.compeleceng.2019.04.011 ↗
22. Hang, J., Zhang, D., Chen, P., Zhang, J., and Wang, B. (2019). Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network. Sensors, 19.10.3390/s19194161 ↗
23. Cucumber leaf disease identification with global pooling dilated convolutional neural network10.1016/j.compag.2019.03.012 ↗
24. Semi-Supervised Learning with Convolutional Neural Networks for UAV images Automatic Recognition10.1016/j.compag.2019.104932 ↗
25. Using deep learning for image-based plant disease detection10.3389/fpls.2016.01419 ↗
26. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
27. Imagenet classification with deep convolutional neural networks
28. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
29. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
30. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
31. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
32. Random forests10.1023/A:1010933404324 ↗
33. Support-Vector Networks10.1007/BF00994018 ↗