Impact of dataset on the study of crop disease image recognition
Keywords:
crop diseases, datasets, transfer learning, deep learning, image recognitionAbstract
Datasets are very important in image recognition research based on machine learning methods. In particular, advanced methods such as deep learning and transfer learning are more dependent on datasets used for training models. The quality of datasets directly affects the final effect of these methods. In the research of crop disease image recognition, due to the complication of the agricultural environment and the variety of crops, datasets are scarce at present. Therefore, more and more researches adopt methods based on transfer learning, which can make up for the lack of data in the target domain with the help of other datasets. Among these methods, the selection of auxiliary domain datasets has great impact on the modeling effect of target domain. In order to clarify the impact of datasets on the research of crop disease image recognition, this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer learning. The selected datasets include PlantVillage and Image Database for Agricultural Diseases and Pests Research (IDADP), which have been widely used in recent studies. And the selected deep neural network frameworks include ResNet50, InceptionV3, and EfficientNet. In the method of this study, the datasets are preprocessed first, such as data enhancement. After dividing the auxiliary domain and the target domain, the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset. Finally, the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target. In the experiments, multiple different datasets and different models were tested and compared. The results show that when the test set samples and training sample scenarios are consistent, the recognition accuracy of different network frameworks on multiple test sets is generally high. When the scenarios of test set samples and training samples are inconsistent, the recognition of various test sets by different network models cannot obtain ideal results. For the recognition of crop disease images that are collected from the actual cultivation environment, the use of IDADP dataset modeling is better, and it has more practical value in the actual application of crop disease image recognition. Keywords: crop diseases, datasets, transfer learning, deep learning, image recognition DOI: 10.25165/j.ijabe.20221505.7005 Citation: Yuan Y, Chen L, Ren Y C, Wang S M, Li Y. Impact of dataset on the study for crop disease image recognition. Int J Agric & Biol Eng, 2022; 15(5): 181–186.References
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Processing, 2019; 9 (1): 51–63.
[5] Wei L, Yue J, Li Z, Kou G, Qu H. Multi-classification detection method of plant leaf disease based on dernel function SVM. Transactions of the CSAM, 2017; 48(S1): 166–171. (in Chinese)
[6] Kaur P, Pannu H S, Malhi A K. Plant disease recognition using fractional-order Zernike moments and SVM classifier. Neural Computing and Applications, 2019; 31(12): 8749–8768.
[7] Zhu Y, Liu D, Chen G, Jia H, Yu H. Mathematical modeling for active and dynamic diagnosis of crop diseases based on Bayesian networks and incremental learning. Mathematical and Computer Modelling, 2013; 58(3-4): 514–523.
[8] Wang X, Zhang C, Zhang S, Zhu Y. Forecasting of cotton diseases and pests based on adaptive discriminant deep belief network. Transactions of the CSAE, 2018; 34(14): 157–164. (in Chinese)
[9] Mohanty S P, Hughes D P, Salathe M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 2016; 7: 1083–1087.
[10] Ngugi L C, Abelwahab M, Abo-Zahhad M. Recent advances in image processing techniques for automated leaf pest and disease recognition - a review. Information Processing in Agriculture, 2021; 8(1): 27–51.
[11] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning. Journal of Big Data, 2016; 3: 9. doi: 10.1186/s40537-016-0043-6.
[12] Edna C T, Li Y, Sam N, Liu Y. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 2018; 161: 272–279.
[13] Yuan Y, Fang S, Chen L. Crop disease image classification based on transfer learning with DCNNs. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2018), LNCS, 2018; 11257: 457–468.
[14] Barbedo J G A. Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 2019; 180: 96–107.
[15] Yang S, Feng Q, Zhang J, Sun W, Wang G. Identification method for potato disease based on deep learning and composite dictionary. Transactions of the CSAM, 2020; 51(7): 22–29. (in Chinese)
[16] Yuan Y, Chen L, Wu H, Li L. Advanced agricultural disease image recognition technologies: A review. Information Processing in Agriculture, 2022; 9(1): 48–59.
[17] Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016; 2016: 3289801. doi: 10.1155/2016/3289801.
[18] Arnal B J G. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture, 2018; 153: 46–53.
[19] Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 2016; 7: 1419. doi: 10.3389/fpls.2016.01419.
[20] Chen L, Yuan Y. Agricultural disease image dataset for disease identification based on machine learning. In: Big Scientific Data Management (BigSDM 2018), LNCS, 2018; 11473: 263–274.
[21] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010; 22(10): 1345–1359.
[22] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), IEEE, 2016; pp.770–778.
[23] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), IEEE, 2016; pp.2818–2826.
[24] Tan M, Le Q V. EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 2019; pp.6105–6114.
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Published
2022-11-01
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Yuan, Y., Chen, L., Ren, Y., Wang, S., & Li, Y. (2022). Impact of dataset on the study of crop disease image recognition. International Journal of Agricultural and Biological Engineering, 15(5), 181–186. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7005
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Information Technology, Sensors and Control Systems
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