Image-based Pretreatment Study of Rice Blast Disease

Authors

  • Zhiwei Shi Graduate School of Advanced Technology and Science Tokushima University, Japan. School of Mechanical Engineering Nantong University, China.
  • Stephen Karungaru Graduate School of Advanced Technology and Science Tokushima University, Japan.
  • Terada Kenji Graduate School of Advanced Technology and Science Tokushima University, Japan.
  • Hongjun Ni School of Mechanical Engineering Nantong University, China.
  • Shuaishuai Lv School of Mechanical Engineering Nantong University, China.
  • Xingxing Wang School of Mechanical Engineering Nantong University, China.
  • Yu Zhu School of Mechanical Engineering Nantong University, China.
  • Yi Lu School of Mechanical Engineering Nantong University, China.

Keywords:

Rice blight, Image pre-processing, Median filtering, Edge Segmentation, Feature Extraction

Abstract

Rice blight has a great impact on rice yield and can lead to yield reduction of up to 70% in severe cases. Traditional detection methods require professional technicians to operate and are costly and inefficient, and cannot detect rice diseases in real-time. In this paper, we applied image detection technology to study rice blast disease based on the Matlab platform. Firstly, a basic rice blast database is built, and then a discussion is made on how to effectively improve the recognition success rate of rice blast images by two aspects: image pre-processing and feature extraction. The main research contents are as follows. (1) After studying the existing plant disease database, a basic rice blast database was constructed by field photography and other means. (2) Preprocessing of the collected rice blight images. Using the algorithm of rgb2gray function in Matlab, the images were grayed out; based on this, median filtering was used for noise reduction; then histogram equalization technique was used for image enhancement to increase the contrast and make the images clear; finally, various segmentation algorithms were used for image segmentation. (3) For the pre-processed rice blight images, feature extraction was performed in terms of the color of the disease to pave the way for feature selection.

References

Guo D, Lu Y, Li JN, et al. Research on rice blast disease identification method based on deep belief network [J]. Agricultural mechanization research,2019, (12): 5.

QI Lu,ZHANG Tao,ZENG Juan,et al. Analysis of the occurrence of major diseases in five major rice production areas in China in recent years[J]. China Plant Protection Journal,2021,41(04):37-42+65.

Zhang Y. H., Wang H., Ma G. M.. Research on image recognition based on deep learning[J]. Modern Information Technology,2019,3(11):111-112+114.

Lu J., Ehsani R., Shi Y., et al. Detection of multi-tomato leaf diseases (late blight, target andbacterial spots) in different stages by using a spectral-based sensor[J]. Scientific Reports,2018, 8(1):1-11.

Shrivastava V K, Pradhan M K. Rice plant disease classification using color features: a machine learning paradigm[J]. Journal of Plant PathoLogy, 2020:1-10.

Hu Y. H., Ping X W, Xu M Z, et al. Detection of late blight disease on potato leaves usinghyperspectral imaging technique[J]. Spectroscopy and Spectral Analysis, 2016, 36(2):515-519.

Chung C L, Huang K L, Chen S Y, et al. Detecting Bakanae disease in rice seedlings bymachine vision[J]. Computers and Electronics in Agriculture, 2016,121:404-411.

Amanda R,Kelsee B,Peter M, et al. Deep learning for image-based cassava disease detection[J]. Frontiers in Plant Science, 2017,8:1852-1859.

Guan Y, Research on fast identification method of rice leaf diseases based on image processing [D]. Northeastern Agricultural University, 2018.

Sun J, Tan WJ, Mao HP, et al. Improved convolutional neural network-based leaf disease identification for a variety of plants[J]. Journal of Agricultural Engineering,2017,33(19):209-215.

Xueqin Zhang,Jiahao Chen,Jingjing Zhuge,et al. Fast plant image recognition based on deep learning[J]. Journal of East China University of Science and Technology (Natural Science Edition) ,2018,44(06):887-895.

Zhao Ze-jun. Occurrence and control measures of rice blight [J]. Modern Agricultural Science and Technology,2018(05):112+114.

Su Ting-Ting,Mou Shao-Min,Dong Meng-Ping,et al. Application of deep migration learning in peanut leaf disease image recognition[J]. Journal of Shandong Agricultural University (Natural Science Edition),2019,50(05):865-869.

Qiu Jing,Liu Jirong,Cao Zhiyong,et al. Research on rice disease image recognition based on convolutional neural network[J]. Journal of Yunnan Agricultural University (Natural Sciences),2019,34(05):884-888.

Liu T T, Wang T, Hu L. Image recognition of rice blight based on convolutional neural network[J]. China Rice Science,2019,33(01):90-94.

Gong Shengrong. Digital image processing and analysis[M]. Tsinghua University Press,2014.

Shao M.Y.,Zhang J.H.,Feng Q.,et al. Research progress of deep learning in plant foliar disease detection and identification[J]. Intelligent Agriculture(in English and Chinese),2022,4(01):29-46.

Yuan Yuan,Chen Lei,Wu Na,et al. Research on image recognition processing method for rice blast disease[J]. Agricultural mechanization research, 2016, 38(06):84-87+92.

Tan Yunlan,Ouyang Chunjuan,Li Long,et al. Research on rice disease image recognition based on deep convolutional neural network[J]. Journal of Jinggangshan University (Natural Science Edition) ,2019,40(02):31-38.

Hu Yuelang. Image detection of rice stripe orange disease based on neural network[D]. Sichuan Agricultural University,2014.

Wang Xue,Guo Xinxin. A review of green crop image segmentation algorithm research[J]. Heilongjiang Science,2018,9(20):36-37.

Xu S.P.,Li L.I.,Jia J.Y.,et al. AR-assisted intelligent analysis and identification system based on HOG-SVM for mobile rice diseases[J]. Journal of Graphology,2021,42(03):454-461.

Kang L, Yuan JQ, Gao R, et al. Hyperspectral imaging for early grading detection of rice blast disease[J]. Spectroscopy and Spectral Analysis,2021,41(03):898-902.

Wang Caixia. Exploration of rice disease identification based on image processing[J]. Agricultural engineering technology,2020,40(33):28-29.

YANG Sen, FENG Quan, ZHANG Jianhua, et al. A potato disease identification method based on deep learning and composite dictionary[J]. Journal of Agricultural Machinery,2020,51(07):22-29.

Tan Yanchao,Zheng Xiaolin,Wei Xiangyu,et al. Metric learning-based multi-spatial recommendation system[J]. Journal of Computer Science,2022,45(01):1-16.

Bao Wenxia,Qiu Xiang,Hu Gensheng,et al. Rice pest identification based on elliptic metric learning space transformation[J]. Journal of South China University of Technology (Natural Science Edition),2020,48(10):136-144.

Wang Wei-Nan,Yang Chao-Hong. A review of target recognition methods based on image processing techniques[J]. Computer and Information Technology,2019,27(06):9-15.

Jiang Xiao,Gao Weiwei,Yang Yile,et al. A convolutional neural network face recognition method based on the Prewitt operator[J]. Software,2019,40(10):16-19.

Amal S. Abdulhussien, Ahmad T. Abdul Saddaa, Kamran Iqbal. Automatic seizure detection with different time delays using SDFT and time-domain feature extraction[J].The Journal of Biomedical Research,2022,36(01):48-57.

Hu YL. Research on the extraction and classification method of fused features of lunar surface impact crater morphology[D]. East China Jiaotong University,2016.

Wang Y, Xie Z, Zhu Chunzhao, et al. Improved adaptive threshold edge detection algorithm based on 3 times B spline wavelet transform[J]. Computational Technology and Automation,2021,40(01):101-103+118.

Yixuan Zou. Deep learning-based segmentation and detection of thyroid eye disease images[D]. Hubei University of Technology, 2020.

Yang, Mi-Kai. Design of DSP-based continuous extraction system for fingerprint image feature points[J]. Electronic Design Engineering,2021,29(08):67-71.

Liu Shuang. Research on image recognition method of cashmere and wool fiber based on BP neural network[D]. Hubei University of Technology, 2020.

Zheng Gengfeng. A target tracking algorithm incorporating effective convolution operon and color histogram[J]. Science and Technology Innovation and Applications,2020(36):73-76.

Yicheng Shi,Dongbo Liu,Yuting Chen. Research on garment image retrieval algorithm based on Surf and improved color moments[J]. Journal of Hunan Engineering College (Natural Science Edition),2021,31(03):53-58.

Chen Y, Zhang W C, Hua Shoutong, Gong Z Y. Automatic diagnosis method of belt conveyor fault based on image recognition[J]. Manufacturing Automation,2022,44(03):205-207+212.

Published

2024-05-30

How to Cite

Zhiwei Shi, Stephen Karungaru, Terada Kenji, Hongjun Ni, Shuaishuai Lv, Xingxing Wang, Yu Zhu, & Yi Lu. (2024). Image-based Pretreatment Study of Rice Blast Disease. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). Retrieved from http://mail.joaasr.com/index.php/joaasr/article/view/945