Detection of spores using polarization image features and BP neural network  

作　　者：Yafei Wang Ning Yang Guoxin Ma Mohamed Farag Taha Hanping Mao Xiaodong Zhang Qiang Shi 

机构地区：[1]School of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China [2]Jiangsu Changdian Technology Co.,Ltd.,Jiangyin 214400,Jiangsu,China [3]School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China [4]Department of Soil and Water Sciences,Faculty of Environmental Agricultural Sciences,Arish University,North Sinai 45516,Egypt [5]School of Science and Technology,Shanghai Open University,Shanghai 200433,China

出　　处：《International Journal of Agricultural and Biological Engineering》2024年第5期213-221,共9页国际农业与生物工程学报（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.32071905,3217895,and 32201686);A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(No.PAPD-2023-87);The National Key Research and Development Program for Young Scientists(Grant 2022YFD2000200);General Program of Basic Science(Natural Science)Research in Higher Education Institutions of Jiangsu Province(Grant 23KJB210004).

摘　　要：Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter’s direction was set to π/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse.

关 键 词：GREENHOUSE SPORES micropolarization image BPNN image processing DETECTION 

分 类 号：S43[农业科学—农业昆虫与害虫防治]