{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:40:49Z","timestamp":1773790849151,"version":"3.50.1"},"reference-count":27,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,9,5]]},"abstract":"<jats:p>In order to solve the problem of long training time and large samples required by traditional image recognition model, a method of crop pest recognition based on transfer learning and data conversion was proposed. It takes CNN models such as Inception V3, VGG16, ResNet as the backbone structure. And the transfer learning was used to improve the model effect. The original picture data was expanded through the transformation of flip, rotation, scale, crop, translation and shading. Based on the data of 11 common pests such as white grub, east asian locust and whitefly etc., the model training and recognition was carried out. The result shows that, the accuracy of transfer learning model is higher than that of non-transfer learning model. The Inception V3 model performs well of all, the recognition accuracy is more than 98.94%. Through the analysis of cross entropy and confusion matrix, data transformation is helpful to improve the accuracy of the model with small sample.<\/jats:p>","DOI":"10.3233\/jcm-226121","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:25:18Z","timestamp":1652185518000},"page":"1697-1709","source":"Crossref","is-referenced-by-count":3,"title":["Small sample and efficient crop pest recognition method based on transfer learning and data transformation"],"prefix":"10.1177","volume":"22","author":[{"given":"Qingfeng","family":"Wei","sequence":"first","affiliation":[{"name":"Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"}]},{"given":"Huan","family":"Li","sequence":"additional","affiliation":[{"name":"CRRC Group Co., Ltd, Hunan, China"}]},{"given":"Changshou","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"},{"name":"Beijing Research Center of Engineering Technology on Rural Distance Information Service, Beijing, China"}]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"},{"name":"Beijing Research Center of Engineering Technology on Rural Distance Information Service, Beijing, China"}]},{"given":"Yaming","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"},{"name":"Beijing Research Center of Engineering Technology on Rural Distance Information Service, Beijing, China"}]},{"given":"Furong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"},{"name":"Beijing Research Center of Engineering Technology on Rural Distance Information Service, Beijing, China"}]},{"given":"Bao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Landscape Bureau of Xinzhou District, Wuhan, Hubei, China"}]}],"member":"179","reference":[{"key":"10.3233\/JCM-226121_ref1","first-page":"92","article-title":"Present situation and methods of agricultural pest control","volume":"3","author":"Song","year":"2021","journal-title":"Agricultural Development and Equipment."},{"key":"10.3233\/JCM-226121_ref2","first-page":"52","article-title":"Problems and solutions in crop pest control","volume":"11","author":"Qing","year":"2021","journal-title":"World Tropical Agricultural Information."},{"key":"10.3233\/JCM-226121_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Frontiers in Plant Science."},{"issue":"1","key":"10.3233\/JCM-226121_ref4","first-page":"1","article-title":"Potato virus Y detection in seed potatoes using deep learning on hyperspectral images","volume":"10","author":"Polder","year":"2019","journal-title":"Front Plant Sci."},{"key":"10.3233\/JCM-226121_ref5","first-page":"805","article-title":"Research on citrus pest and disease recognition Method in Guangxi Based on regional convolutional neural network model","volume":"4","author":"Su","year":"2020","journal-title":"Southwest China Journal of Agricultural Sciences."},{"issue":"3","key":"10.3233\/JCM-226121_ref6","first-page":"75","article-title":"Identification and morphological analysis of adult spodoptera frugiperda and its close related species using deep learning","volume":"2","author":"Wei","year":"2020","journal-title":"Intelligence Agriculture."},{"issue":"1","key":"10.3233\/JCM-226121_ref7","first-page":"154","article-title":"Image recognition of stored grain Pests: Based on Deep Convolutional Neural Network","volume":"31","author":"Chen","year":"2018","journal-title":"Chinese Agricultural Science Bulletin."},{"key":"10.3233\/JCM-226121_ref8","first-page":"156","article-title":"Localization and recognition of pests in tea plantation based on image saliency analysis and convolutional neural network","volume":"6","author":"Yang","year":"2017","journal-title":"Transactions of the Chinese Society of Agricultural Engineering."},{"key":"10.3233\/JCM-226121_ref9","first-page":"65","article-title":"Insect classification with heirarchical deep convolutional neural networks","author":"Glickd","year":"2017","journal-title":"Convolutional neural networks for visual recongnition. 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