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Identifying and counting phyllotreta striolata
fabriciuson based on yellow sticky trap |
1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China; 2. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong 266033, China; 3. Guangdong Provincial Key Laboratory of High Technology for Plant Protection, Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, Guangdong 510640, China
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Abstract To grasp the occurrence of phyllotreta striolatas fabricius with serious damage to vegetable growth and the damage extent, a method for identifying and counting phyllotreta striolatas fabricius was proposed based on yellow sticky traps. The maximum interclass variance algorithm（OTSU） algorithm was used to segment yellow sticky traps from the background images. OTSU algorithm, color smooth and active contour model were applied to segment phyllotreta striolatas fabricius from yellow sticky traps. Color feature, textural feature and shape feature of candidate areas were subsequently extracted, and support vector machine was built to identify and count the phyllotreta striolatas fabricius. The results show that the proposed method can achieve high accuracy of 88.16％, precision of 92.00％ and recall of 81.56％, and the information of phyllotreta striolata fabricius can be obtained in realtime.
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| Fund:广东省重点领域研发计划项目（2019B020217003，2019B020214002） |
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