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Classification of weed seeds based on visual images and deep learning

农业信息处理(英文) 2023年第1期10卷 40-51页

作者： Tongyun Luo Jianye Zhao Yujuan Gu Shuo Zhang Xi Qiao Wen Tian Yangchun Han College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100PR China Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhen 518120PR China Qingdao China-Agri IOT Science&Technology Co.Ltd. Qingdao 266106PR China Guangzhou Customs Districk Technology Center Guangzhou 510623PR China Jiangyin Customs Jiangyin 214400PR China

Weeds are mainly spread by weed seeds being mixed with agricultural and forestry crop seeds,grain,animal hair,and other plant products,and disturb the growing environment of target plants such as crops and wild native plants.The accurate and efficient classification of weed seeds is important for the effective management and control of weeds.However,classification remains mainly dependent on destructive sampling-based manual inspection,which has a high cost and rather low flux.We considered that this problem could be solved using a nondestructive intelligent image recognition method.First,on the basis of the establishment of the image acquisition system for weed seeds,images of single weed seeds were rapidly and completely segmented,and a total of 47696 samples of 140 species of weed seeds and foreign materials remained.Then,six popular and novel deep Convolutional Neural Network(CNN)models are compared to identify the best method for intelligently identifying 140 species of weed seeds.Of these samples,33600 samples are randomly selected as the training dataset for model training,and the remaining 14096 samples are used as the testing dataset for model *** and GoogLeNet emerged from the quantitative evaluation as the best *** has strong classification accuracy and efficiency(low time consumption),and GoogLeNet has the best classification accuracy.A suitable CNN model for weed seed classification could be selected according to specific identification accuracy requirements and time costs of applications.This research is beneficial for developing a detection system for weed seeds in various applications.The resolution of taxonomic issues and problems associated with the identi-fication of these weed seeds may allow for more effective management and control.

关键词： seed identification Image acquisition system Multi-object classification Convolutional neural network Computer vision

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Discrimination of Acacia seeds at species and subspecies levels using an image analyzer

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Forestry Studies in China 2013年第4期15卷 253-260页

作者： V.SIVAKUMAR R.ANANDALAKSHMI Rekha R.WARRIER B.G.SINGH M.TIGABU B.NAGARAJAN Division of seed Technology Institute of Forest Genetics and Tree Breeding Indian Council of Forestry Research and Education Swedish University of Agricultural Sciences Southern Swedish Forest Research Centre

seeds of Acacia species and subspecies were characterized using an image analyzer and discriminated for the purpose of identification of species, using their seeds. The species considered in the study were Acacia nilotica subsp. indica, A. nilotica subsp. cupressiformis, A. nilotica subsp. tomentosa, A. tortilis subsp. raddiana, A. tortilis subsp. spirocarpa, A. raddiana, A. senegal, A. auriculiformis, A. farnesiana, A. leucophloea, A. mearnsii, A. melanoxylon, A. planifrons and A. mangium. Eight samples each consisting of 25 seeds per species were studied using the image analyzer for physical characteristics of seeds, such as 2D surface area, length, width, perimeter, roundness, aspect ratio and fullness ratio. Discriminant analysis showed that acacias can be discriminated at species and subspecies levels, with 96% accuracy. Exceptions were A. nilotica subsp. tomentosa(75.0%), A. tortilis subsp. spirocarpa(75.0%) and A. raddiana(87.5%) which had relatively low discrimination accuracy. However, discriminant analysis within selected species showed complete recognition of these species except for A. tortilis subsp. spirocarpa, that had still a large overlap with A. leucophloea. The study also revealed that both seed size and shape characteristics were responsible for species discrimination. It can be concluded that rapid analysis of seed size and shape characteristics using image analysis techniques can be used as primary and secondary keys for identification of acacias.

关键词： Acacia image analyzer discriminant analysis seed identification

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