Abstract
With the application of IOT technology in maize disease images for monitoring and collecting, timely detection of the types and characteristics of identification of disease has become a hot research in the diagnosis and treatment of diseases and insect pests. In order to improve the recognition accuracy of maize leaf, achieve rapid diagnostic purposes, this paper takes the leaf spot of maize gray leaf spot and image as the research object, use the computer image processing technology is studied on the effective segmentation and recognition of color and shape features. The genetic algorithm was adopted to optimize the selection of maize disease images real-time filtering; \( 3 * 3 \) mode noise suppression of the image selected by value smoothing; then select the HSV component of the color feature extraction of the disease; the maximum between class variance (OTSU) disease shape character segmentation and recognition. The results show that, based on genetic algorithm optimization based on image In HSV and Otsu method can be more accurate segmentation and recognition of the disease of color and shape features, and enhance the real-time and accuracy of the image of maize disease detection and recognition and oriented under the condition of things plant diseases and insect pests of maize and provide technical support.
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References
Horiuchi, T., Ibrahim, A., Kadoi, H., Tominaga, S.: An effective method for illumination-invariant representation of color images. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7584, pp. 401–410. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33868-7_40
Karaboga, D., Basturk, B.: On the performance of artificial bee colony algorithm. Swarm Evol. Comput. 2(1), 39–52 (2012)
Si, X.: A study of image processing and identifying technology on cucumber disease. Master thesis, Jilin Agricultural University (2006)
Liu, Y., Guo, Y.: Gray-scale histograms feature extraction using MATLAB. Comput. Knowl. Technol. 5(32), 9032–9034 (2009)
Wang, N., Wang, K., Xie, R., Lai, J., Ming, B., Li, S.: Maize leaf disease identification based on fisher discrimination analysis. Sci. Agric. Sin. 42(11), 3836–3842 (2009)
Zhong, Q., Zhou, J., Wu, Q., Wang, H., Lei, T.: A Method of Line Tracking Based on Hough Transforms and Edge Histogram. Opto-Electronic Engineering, 41(3), 89–94, March 2014
Chen, Y., Ye, F., Xiao, F., Li, Q.: Detection and recognition of traffic signs based on HSV vision model and shape features. J. Jianghan Univ. (Nat. Sci. Ed.) 44(2), 119–125, April 2016
Yang, J., Zhu, L.: Color Image Segmentation Method Based on RGB Color Space. ISUANJIYU XIANDAIHUA (2010)08-0147-03
Wen, C., Wang, S., Yu, H., Su, H.: Image segmentation method for maize diseases based on pulse coupled neural network with modified artificial bee algorithm. Transactions of the Chinese Society of Agricultural Engineering, July 2013
Zhang, J., Zhuang, J., Du, H., et al.: Self-organizing genetic algorithm based tuning of PID controllers. Inf. Sci. 179(7), 1007–1018 (2009)
Li, F., Xu, L., Jin, C., et al.: Intelligent bionic genetic algorithm (IB-GA) and its convergence. Expert. Syst. Appl. 38(7), 8804–8811 (2011)
Hu, Y.: On the development of maize seed industry the 20 century. Nanjing Agricultural University. In Partial Fulfillment of the Requirements for the Doctor Degree (2011)
Bao, H., Fan, Y., Rao, C., Zhang, Y., Dai, Y.: Fast median filtering algorithm based on mean searching. J. Sichuan Univ. (Eng. Sci. Ed.), March 2011
Zhang, Y., Han, X., Wang, K.: Study on corn variety identification based on depth and color features of gray histogram. Crops 1, 156–159 (2015)
Li, W.: Research on the cucumber leaf diseases recognition based on image processing. Master thesis, Hunan Agricultural University (2013)
Zong, H.L.: Research and Application of Maize Leaf Disease Fuzzy Recognition Method Based on Image Characteristics. Beijing Posts and Telecommunications University (2010)
Liu, Z., Kang, J.: A brief analysis on meteorological conditions of several crop diseases. Hei Longjiang Agric. Sci. (1), 157 (2013)
Li, S.: Biological control of soilborn Phythopathogens including Ralastonia solanacearum, Phytophtthora capsici, and Meloidogyne incognita on pepper and tomato. Nanjing Agricultural University (2008)
Tan, R.: Research of Weed Recognition Method Using Color and Shape Features. JiangSu University (2009)
Hu, Y., Ji, H., Wu, P., Xuan, X.: Research on image filtering algorithm and its FPGA implementation. Comput. Meas. Control (2008). 11-1672-04 GCIS 2009. WRI Global Congress on, 2009
Ping, L.: Comparison of image smoothing processing methods. Inf. Technol. (2010). 34: 01-0065-03
Ma, Y., Yun, W.: Research progress of genetic algorithm. Appl. Res. Comput. 29(4), 1201–1206, 1210, April 2012
Su, H.: Design and Implementation of Maize Disease Image Recognition System. JiLin University (2009)
Acknowledgments
This work was funded by the China Spark Program. 2015GA66004. “Integration and demonstration of corn precise operation technology based on Internet of things”.
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Chen, G., Meng, Y., Lu, J., Wang, D. (2019). Research on Color and Shape Recognition of Maize Diseases Based on HSV and OTSU Method. In: Li, D. (eds) Computer and Computing Technologies in Agriculture X. CCTA 2016. IFIP Advances in Information and Communication Technology, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-030-06155-5_30
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DOI: https://doi.org/10.1007/978-3-030-06155-5_30
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