Abstract
There are many related diseases in the process of crop planting, which reduces the quality and yield of crops. Faced with such a situation, the prevention of crop diseases has become a hot spot and has broad application prospects. This experiment uses the image recognition technology of machine vision to analyze and recognize crop diseases. Based on the features of machine vision that can capture details that cannot be observed by the human eye, with high accuracy and high efficiency, it provides accurate image recognition of crop diseases. In accordance with. In the process of selecting the SVM classifier for image classification, the kernel function and gamma parameters in the classifier were adjusted, and the kernel function and high accuracy parameter interval suitable for crop disease analysis were found.
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References
Kulkarni, O.: Crop disease detection using deep learning. In: International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–4 (2018)
Zhu, S., Zhang, J., Shuai, G., Hongli, L., Zhang, F., Dong, Z.: Autumn crop mapping based on deep learning method driven by historical labelled dataset. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 4669–4672 (2020)
Kalimuthu, M., Vaishnavi, P., Kishore, M.: Crop prediction using machine learning. In: International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 926–932 (2020)
Medar, R., Rajpurohit, V.S., Shweta, S.: Crop yield prediction using machine learning techniques. In: IEEE 5th International Conference for Convergence in Technology (I2CT), pp. 1–5 (2019)
Xu, Q., Zhang, J., Zhang, F., Zhu, S.: Develop large-area autumn crop type product using a deep learning strategy. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 4673–4676 (2020)
Singh, J., Mahapatra, A., Basu, S., Banerjee, B.: Assessment of Sentinel-1 and Sentinel-2 satellite imagery for crop classification in Indian region during Kharif and Rabi crop cycles. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 3720–3723 (2019)
Kumar, A., Sarkar, S., Pradhan, C.: Recommendation system for crop identification and pest control technique in agriculture. In: International Conference on Communication and Signal Processing (ICCSP), pp. 0185–0189 (2019)
Verma, G., Taluja, C., Saxena, A.K.: Vision based detection and classification of disease on rice crops using convolutional neural network. In: International Conference on Cutting-Edge Technologies in Engineering (ICon-CuTE), pp. 1–4 (2019)
Kang, J., Zhang, H., Yang, H., Zhang, L.: Support vector machine classification of crop lands using Sentinel-2 imagery. In: International Conference on Agro-geoinformatics (Agro-geoinformatics), pp. 1–6 (2018)
Chu, H., Zhang, D., Shao, Y., Chang, Z., Guo, Y., Zhang, N.: Using HOG descriptors and UAV for crop pest monitoring. In: Chinese Automation Congress (CAC), pp. 1516–1519 (2018)
Hu, H., Su, C., Yu, P.: Research on pest and disease recognition algorithms based on convolutional neural network. In: International Conference on Virtual Reality and Intelligent Systems (ICVRIS), pp. 166–168 (2019)
Pujari, J.D., Yakkundimath, R., Byadgi, A.S.: Identification and classification of fungal disease affected on agriculture/horticulture crops using image processing techniques. In: IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–4 (2014)
Militante, S.V., Gerardo, B.D., Medina, R.P.: Sugarcane disease recognition using deep learning. In: IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), pp. 575–578 (2019)
Park, H., Eun, J., Kim, S.: Image-based disease diagnosing and predicting of the crops through the deep learning mechanism. In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 129–131 (2017)
Emon, S.H., Mridha, M.A.H., Shovon, M.: Automated recognition of rice grain diseases using deep learning. In: International Conference on Electrical and Computer Engineering (ICECE), pp. 230–233 (2020)
Prashar, K., Talwar, R., Kant, C.: CNN based on overlapping pooling method and multi-layered learning with SVM & KNN for American cotton leaf disease recognition. In: International Conference on Automation, Computational and Technology Management (ICACTM), pp. 330–333 (2019)
Nikhitha, M., Roopa Sri, S., Uma Maheswari, B.: Fruit recognition and grade of disease detection using inception V3 model. In: Communication and Aerospace Technology (ICECA), pp. 1040–1043 (2019)
Ai, Y., Sun, C., Tie, J., Cai, X.: Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access 8, 171686–171693 (2020)
Genaev, M., Ekaterina, S., Afonnikov, D.: Application of neural networks to image recognition of wheat rust diseases. In: Genomics and Bioinformatics (CSGB), pp. 40–42 (2020)
Liu, J., Lv, F., Di, P.: Identification of sunflower leaf diseases based on random forest algorithm. In: Automation and Systems (ICICAS), pp. 459–463 (2019)
Zhu, S., Xu, C., Wang, J., Xiao, Y., Ma, F.: Research and application of combined kernel SVM in dynamic voiceprint password authentication system. In: IEEE 9th International Conference on Communication Software and Networks (ICCSN), pp. 1052–1055 (2017)
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Zhou, Y., Zhang, K., Shi, Y., Cui, P. (2022). A Crop Disease Recognition Algorithm Based on Machine Learning. In: Jiang, D., Song, H. (eds) Simulation Tools and Techniques. SIMUtools 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-97124-3_38
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DOI: https://doi.org/10.1007/978-3-030-97124-3_38
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