Research on Crop Disease Recognition Database System Based on Image Content Retrieval

Abstract

Abstract:

In view of the shortcomings for crop disease diagnosis expert system, the poper puts forward the way that disease diagnosis diseases based on image retrieval.

Try to establish image characteristics library database, knowledge base , image library and so on according to the plant disease images. Diagnosis, extract the characteristics

of the plant disease image, the characteristics and features of library matching query, through the query results for a description of the disease and prevention measures. In

order to improve the retrieval precision and recall rate, the poper mainly explores the characteristics of library information in the disease image database creation access and

retrieval algorithm design. Disease spot in tobacco plant disease image, for example, the color disease spot edge detection of based multifeature selection and on support vector

machine , 25 disease spot feature extracting, using double coding genetic algorithm and support vector machine (SVM) for feature dimension reduction, in order to get effective

characterization of disease features 17 and the corresponding weights, the characteristics of the normalized processing after establishing a database, and retrieval algorithm is

designed. Experiments show that the constructed image retrieval system has high precision and recall, the fusion of multiple features of disease spot retrieval precision is

higher than single feature. Diagnosis of disease in this way that includes higher disease recognition rate and diagnosis of visualization, the way is used for crop disease

diagnosis expert system that can improve the robustness of the system and achieve the remote online diagnosis of the disease.

Key words: content-based disease image searching, disease spot segmentation, feature extraction, image database, image similarity

PU Yong-xian. Research on Crop Disease Recognition Database System Based on Image Content Retrieval[J]. Computer and Modernization.

References

［1］ Georgieva K, Georgieva Y, Daskalov D. Theoretical substantiation of model of system for evaluation a state of vine plants and taking a decision for plant protection activities［

J］. Trakia Journal of Sciences (Series Social Sciences), 2003,1:30-32.
［2］穗波信雄. 图像处理在植物生长信息提取中的应用［R］. 农业机械学会关西支部研究会资料(日), 1992:63-46.
［3］范启福，赖荣泉，丘启发，等. 烟草病虫害远程在线诊断系统的建立及应用［J］. 中国烟草学报, 2008,29(1):60-61.
［4］张汉千，赖荣泉，林天然，等. 利用手机进行烟草病虫害求助与诊断［J］. 中国烟草科学, 2009,30(1):78-79.
［5］姜红花. 基于Web GIS烟草病虫害发生动态预测及诊断系统的开发［J］. 山东农业大学学报(自然科学版), 2010,41(4):631-635.
［6］彭署光. 烟草病害专家系统的建立与应用［J］. 湖南农业大学学报, 2010(6):181-184.
［7］管泽鑫,唐健,杨保军,等. 基于图像的水稻病害识别方法研究［J］. 中国水稻科学, 2010,24(5):497-502.
［8］王军英. 基于BP神经网络的葡萄病害诊断系统研究［J］. 农业信息网, 2013,8:30-33.
［9］谭克竹,沈维政. 基于BP神经网络的大豆叶片病害诊断模型的研究［J］. 控制理论与应用, 2013,32(12):5-7. 
［10］张云飞. 基于量子神经网络和组合特征参数的玉米叶部病害识别［J］. 南方农业报, 2013,44(8):1286-1290.
［11］刘鹏,屠康,徐洪蕊,等. 基于支持向量机的甜柿表面病害识别［J］. 现代食品科技, 2011,27(3):349-353.
［12］田有文,李天来,李成华,等. 基于支持向量机的葡萄病害图像识别方法［J］. 农业工程学报, 2007,23(6)：175-179.
［13］张铮，王艳平，薛桂香，等. 数字图像处理与机器视觉-Visual C+〖KG-*3〗+与Matlab实现［M］. 北京：人民邮电出版社, 2012：156-162.
［14］濮永仙. 基于支持向量机与多特征选择的作物彩色病斑边缘检测［J］. 计算机应用系统, 2014(8)待发.
［15］Lindgreen R, Herschberg I. On the validity of the bell-lapadula model［J］. Computer& Security, 1994,13:317-338.
［16］Otsu N A. Threshold selection method from gray-level histo-gram［J］. IEEE Trans. Syst. Man. Cybenet, 1979,9(15)：652-655.
［17］濮永仙,余翠兰. 基于双编码遗传算法的支持向量机作物病害图像识别方法［J］. 贵州农业科学, 2013,7(41)：187-191.
［18］Gersho A, Gray R M. Vector Quantization and Signal Compression［M］. Norwell，Massachusetts，USA: Kluwer Academic Publishers， 1992．

[1]	WANG Qiu-yi, ZHOU Hao, ZHENG Ting-ting. Improved RetinaNet Target Detection Method for Power Equipment [J]. Computer and Modernization, 2024, 0(01): 47-52.
[2]	YANG Liu-qing, WANG Chong. Personalized Recommendation Method of Web Service Resources Based on Maximum Entropy [J]. Computer and Modernization, 2023, 0(09): 32-37.
[3]	HU Rui-jie, CHE Dou. Review of Infrared Small Target Detection [J]. Computer and Modernization, 2023, 0(08): 79-86.
[4]	WANG Xin-yi, YIN Si-qing, HONG Jun. Asymmetric Deep Supervised Hashing with Attention Mechanism [J]. Computer and Modernization, 2023, 0(05): 26-31.
[5]	PAN Feng, WANG Jie, ZHANG Yan-sha, TAN Mian, HE Xing, WANG Lin, . Person Re-identification Based on Dual-branch Feature Concatenation [J]. Computer and Modernization, 2023, 0(05): 93-99.
[6]	WANG Hao-chang, LIU Ru-yi. Review of Relation Extraction Based on Pre-training Language Model [J]. Computer and Modernization, 2023, 0(01): 49-57.
[7]	ZHANG Xiao-dong, WANG Xu-ying, QIN Zi-xuan. Pump Detection Period Predicting of Pump Well Based on Feature Fusion [J]. Computer and Modernization, 2022, 0(12): 60-66.
[8]	LI Chun-sheng, FENG Yang-xiao, FU Yu, ZHANG Ke-jia, WU Run-tong. Employee Behavior Analysis Method Based on Mean Clustering [J]. Computer and Modernization, 2022, 0(09): 19-24.
[9]	WANG Meng, ZHANG Hong-xin, LIU Qing-hua, ZHANG Dong. Western Blot Image Recognition of Helicobacter Pylori Based on Improved YOLOv5 [J]. Computer and Modernization, 2022, 0(09): 78-84.
[10]	CHEN Gang. Government Hotline Work-order Classification Fusing RoBERTa and Feature Extraction [J]. Computer and Modernization, 2022, 0(06): 21-26.
[11]	HE Xuan, LIU Yi-xin, HE Xiao-hai, QING Lin-bo, CHEN Hong-gang. Gait Feature Recognition Based on Improved Residual Network and Joint Loss Function [J]. Computer and Modernization, 2022, 0(04): 27-32.
[12]	ZHAO Cong-hui, FENG Qing-sheng. Rotation Invariant Texture Extraction of Wheel Tread Based on DT-CWT and SVM [J]. Computer and Modernization, 2021, 0(12): 85-90.
[13]	LI Long, ZHANG Chong-yang. Vehicle Taillight Detection Method Based on Improved YOLOv3 [J]. Computer and Modernization, 2021, 0(07): 89-94.
[14]	LIU Ya-dong, LUO Yin-sheng, CAO Yang-yang, SONG Wei. Application of Deep Learning in Defect Detection of Mobile Phone Data Interface [J]. Computer and Modernization, 2021, 0(06): 35-40.
[15]	LIN Zhi-wei, ZHU Wen-zhang, CHEN Hao. Dynamic Gesture Recognition Based on Space-time Feature Extraction of Neural Network [J]. Computer and Modernization, 2021, 0(06): 41-47.