{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T03:41:10Z","timestamp":1768534870624,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["SWU019015"],"award-info":[{"award-number":["SWU019015"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11760-023-02711-y","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T08:02:35Z","timestamp":1691568155000},"page":"63-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Monitoring of impurities in green peppers based on convolutional neural networks"],"prefix":"10.1007","volume":"18","author":[{"given":"Jian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"an","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengxin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengsong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"2711_CR1","doi-asserted-by":"publisher","first-page":"696","DOI":"10.3390\/v11080696","volume":"11","author":"M Cao","year":"2019","unstructured":"Cao, M., Zhang, S., Li, M., Liu, Y., Dong, P., Li, S., Kuang, M., Li, R., Zhou, Y.: Discovery of four novel viruses associated with flower yellowing disease of green SichuanPepper (Zanthoxylum Armatum) by virome analysis. Viruses-basel. 11, 696 (2019). https:\/\/doi.org\/10.3390\/v11080696","journal-title":"Viruses-basel."},{"key":"2711_CR2","doi-asserted-by":"publisher","DOI":"10.1093\/fqsafe\/fyac043","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zhou, H., Luo, F., Wan, L., Li, C., Wang, L.: Determination of mechanical properties of Zanthoxylum armatum using the discrete element method. Food Qual. Saf. (2022). https:\/\/doi.org\/10.1093\/fqsafe\/fyac043","journal-title":"Food Qual. Saf."},{"key":"2711_CR3","doi-asserted-by":"publisher","first-page":"162206","DOI":"10.1109\/ACCESS.2019.2946589","volume":"7","author":"Y Shen","year":"2019","unstructured":"Shen, Y., Yin, Y., Zhao, C., Li, B., Wang, J., Li, G., Zhang, Z.: Image recognition method based on an improved convolutional neural network to detect impurities in wheat. IEEE Access. 7, 162206\u2013162218 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2946589","journal-title":"IEEE Access."},{"key":"2711_CR4","doi-asserted-by":"publisher","first-page":"1066115","DOI":"10.3389\/fpls.2022.1066115","volume":"13","author":"P Wang","year":"2022","unstructured":"Wang, P., Luo, F., Wang, L., Li, C., Niu, Q., Li, H.: S-ResNet: an improved ResNet neural model capable of the identification of small insects. Front. Plant Sci. 13, 1066115 (2022). https:\/\/doi.org\/10.3389\/fpls.2022.1066115","journal-title":"Front. Plant Sci."},{"key":"2711_CR5","doi-asserted-by":"crossref","unstructured":"Ankam, P., Shankar, V., Harshini, P., Akash, A., Valusa, A.: Real time face identification for capturing the class attendance using convolutional neural networks. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (2021)","DOI":"10.1109\/ICICCS51141.2021.9432334"},{"issue":"1","key":"2711_CR6","doi-asserted-by":"publisher","first-page":"e13942","DOI":"10.1002\/ep.13942","volume":"42","author":"J Pu","year":"2023","unstructured":"Pu, J., Zhu, S., Miao, Y., Huang, H.: Detection of dish waste degree based on image processing and convolutional neural networks. Environ. Progress Sustain. Energy 42(1), e13942 (2023). https:\/\/doi.org\/10.1002\/ep.13942","journal-title":"Environ. Progress Sustain. Energy"},{"key":"2711_CR7","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.compag.2017.08.005","volume":"141","author":"X Cheng","year":"2017","unstructured":"Cheng, X., Zhang, Y., Chen, Y., Wu, Y., Yue, Y.: Pest identification via deep residual learning in complex background. Comput. Electron. Agric. 141, 351\u2013356 (2017). https:\/\/doi.org\/10.1016\/j.compag.2017.08.005","journal-title":"Comput. Electron. Agric."},{"key":"2711_CR8","doi-asserted-by":"publisher","first-page":"105174","DOI":"10.1016\/j.compag.2019.105174","volume":"169","author":"Y Li","year":"2020","unstructured":"Li, Y., Wang, H., Dang, L.M., Sadeghi-Niaraki, A., Moon, H.: Crop pest recognition in natural scenes using convolutional neural networks. Comput. Electron. Agric. 169, 105174 (2020). https:\/\/doi.org\/10.1016\/j.compag.2019.105174","journal-title":"Comput. Electron. Agric."},{"key":"2711_CR9","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3233\/JIFS-179413","volume":"38","author":"L Deng","year":"2020","unstructured":"Deng, L., Wang, Z., Wang, C., He, Y., Huang, T., Dong, Y., Zhang, X.: Application of agricultural insect pest detection and control map based on image processing analysis. IFS 38, 379\u2013389 (2020). https:\/\/doi.org\/10.3233\/JIFS-179413","journal-title":"IFS"},{"key":"2711_CR10","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s11760-021-02029-7","volume":"16","author":"Z Xiao","year":"2022","unstructured":"Xiao, Z., Yin, K., Geng, L., Wu, J., Zhang, F., Liu, Y.: Pest identification via hyperspectral image and deep learning. SIViP 16, 873\u2013880 (2022). https:\/\/doi.org\/10.1007\/s11760-021-02029-7","journal-title":"SIViP"},{"key":"2711_CR11","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1007\/s11760-023-02534-x","volume":"17","author":"L Song","year":"2023","unstructured":"Song, L., Liu, M., Liu, S., Wang, H., Luo, J.: Pest species identification algorithm based on improved YOLOv4 network. SIViP 17, 3127\u20133134 (2023). https:\/\/doi.org\/10.1007\/s11760-023-02534-x","journal-title":"SIViP"},{"key":"2711_CR12","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s11760-022-02261-9","volume":"17","author":"S Qian","year":"2023","unstructured":"Qian, S., Du, J., Zhou, J., Xie, C., Jiao, L., Li, R.: An effective pest detection method with automatic data augmentation strategy in the agricultural field. SIViP 17, 563\u2013571 (2023). https:\/\/doi.org\/10.1007\/s11760-022-02261-9","journal-title":"SIViP"},{"key":"2711_CR13","doi-asserted-by":"publisher","first-page":"104978","DOI":"10.1016\/j.compag.2019.104978","volume":"166","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Liu, H., Meng, Z., Chen, J.: Deep learning-based automatic recognition network of agricultural machinery images. Comput. Electron. Agric. 166, 104978 (2019). https:\/\/doi.org\/10.1016\/j.compag.2019.104978","journal-title":"Comput. Electron. Agric."},{"key":"2711_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.25165\/j.ijabe.20181103.3454","volume":"11","author":"K Yang","year":"2018","unstructured":"Yang, K., Hui, L., Pei, W., Meng, Z., Chen, J.: Convolutional neural network-based automatic image recognition for agricultural machinery. Int. J. Agric. Biol. Eng.. 11, 7 (2018). https:\/\/doi.org\/10.25165\/j.ijabe.20181103.3454","journal-title":"Int. J. Agric. Biol. Eng.."},{"key":"2711_CR15","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/s11119-017-9553-2","volume":"19","author":"X Liu","year":"2018","unstructured":"Liu, X., Jia, W., Ruan, C., Zhao, D., Gu, Y., Chen, W.: The recognition of apple fruits in plastic bags based on block classification. Precision Agric. 19, 735\u2013749 (2018). https:\/\/doi.org\/10.1007\/s11119-017-9553-2","journal-title":"Precision Agric."},{"key":"2711_CR16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.inpa.2019.07.003","volume":"7","author":"MK Tripathi","year":"2020","unstructured":"Tripathi, M.K., Maktedar, D.D.: A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: a survey. Inf. Process. Agric. 7, 183\u2013203 (2020). https:\/\/doi.org\/10.1016\/j.inpa.2019.07.003","journal-title":"Inf. Process. Agric."},{"key":"2711_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.6041\/j.issn.1000-1298.2018.11.003","volume":"49","author":"J Xiong","year":"2018","unstructured":"Xiong, J., Liu, Z., Lin, R., Chen, S., Chen, W., Yang, Z.: Unmanned aerial vehicle vision detection technology of green mango on tree in natural environment. Trans. Chin. Soc. Agric. Mach. 49, 23\u201329 (2018). https:\/\/doi.org\/10.6041\/j.issn.1000-1298.2018.11.003","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"2711_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.6041\/j.issn.1000-1298.2018.04.005","volume":"49","author":"J Xiong","year":"2018","unstructured":"Xiong, J., Zhen, L.: Visual detection technology of green citrus under natural environment. Trans. Chin. Soc. Agric. Mach. 49, 45\u201352 (2018). https:\/\/doi.org\/10.6041\/j.issn.1000-1298.2018.04.005","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"2711_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-023-02498-y","author":"R Bora","year":"2023","unstructured":"Bora, R., Parasar, D., Charhate, S.: A detection of tomato plant diseases using deep learning MNDLNN classifier. SIViP (2023). https:\/\/doi.org\/10.1007\/s11760-023-02498-y","journal-title":"SIViP"},{"key":"2711_CR20","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1007\/s11760-020-01706-3","volume":"14","author":"J Mun","year":"2020","unstructured":"Mun, J., Kim, J.: Universal super-resolution for face and non-face regions via a facial feature network. SIViP 14, 1601\u20131608 (2020). https:\/\/doi.org\/10.1007\/s11760-020-01706-3","journal-title":"SIViP"},{"key":"2711_CR21","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1007\/s11760-022-02345-6","volume":"17","author":"E Alqaralleh","year":"2023","unstructured":"Alqaralleh, E., Afaneh, A., Toygar, \u00d6.: Masked face recognition using frontal and profile faces with multiple fusion levels. SIViP 17, 1375\u20131382 (2023). https:\/\/doi.org\/10.1007\/s11760-022-02345-6","journal-title":"SIViP"},{"key":"2711_CR22","doi-asserted-by":"publisher","first-page":"045011","DOI":"10.1088\/2632-2153\/ac9cb5","volume":"3","author":"N Ghielmetti","year":"2022","unstructured":"Ghielmetti, N., Loncar, V., Pierini, M., Roed, M., Summers, S., Aarrestad, T., Petersson, C., Linander, H., Ngadiuba, J., Lin, K., Harris, P.: Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. Mach. Learn. Sci. Technol. 3, 045011 (2022). https:\/\/doi.org\/10.1088\/2632-2153\/ac9cb5","journal-title":"Mach. Learn. Sci. Technol."},{"key":"2711_CR23","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s11265-020-01614-2","volume":"93","author":"J Wan","year":"2021","unstructured":"Wan, J., Ding, W., Zhu, H., Xia, M., Huang, Z., Tian, L., Zhu, Y., Wang, H.: An efficient small traffic sign detection method based on YOLOv3. J Sign Process Syst. 93, 899\u2013911 (2021). https:\/\/doi.org\/10.1007\/s11265-020-01614-2","journal-title":"J Sign Process Syst."},{"key":"2711_CR24","doi-asserted-by":"publisher","first-page":"105900","DOI":"10.1016\/j.compag.2020.105900","volume":"180","author":"Q Li","year":"2021","unstructured":"Li, Q., Jia, W., Sun, M., Hou, S., Zheng, Y.: A novel green apple segmentation algorithm based on ensemble U-Net under complex orchard environment. Comput. Electron. Agr. 180, 105900 (2021). https:\/\/doi.org\/10.1016\/j.compag.2020.105900","journal-title":"Comput. Electron. Agr."},{"key":"2711_CR25","doi-asserted-by":"publisher","first-page":"105932","DOI":"10.1016\/j.compag.2020.105932","volume":"181","author":"Y Li","year":"2021","unstructured":"Li, Y., Li, M., Qi, J., Zhou, D., Zou, Z., Liu, K.: Detection of typical obstacles in orchards based on deep convolutional neural network. Comput. Electron. Agr. 181, 105932 (2021). https:\/\/doi.org\/10.1016\/j.compag.2020.105932","journal-title":"Comput. Electron. Agr."},{"key":"2711_CR26","doi-asserted-by":"publisher","first-page":"106780","DOI":"10.1016\/j.compag.2022.106780","volume":"194","author":"J Qi","year":"2022","unstructured":"Qi, J., Liu, X., Liu, K., Xu, F., Guo, H., Tian, X., Li, M., Bao, Z., Li, Y.: An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Comput. Electron. Agric. 194, 106780 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"2711_CR27","doi-asserted-by":"publisher","first-page":"1053329","DOI":"10.3389\/fpls.2022.1053329","volume":"13","author":"P Wang","year":"2022","unstructured":"Wang, P., Tang, Y., Luo, F., Wang, L., Li, C., Niu, Q., Li, H.: Weed25: a deep learning dataset for weed identification. Front. Plant Sci. 13, 1053329 (2022). https:\/\/doi.org\/10.3389\/fpls.2022.1053329","journal-title":"Front. Plant Sci."},{"key":"2711_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/wat2.1518","author":"J Gonz\u00e1lez-Camejo","year":"2021","unstructured":"Gonz\u00e1lez-Camejo, J., Ferrer, J., Seco, A., Barat, R.: Outdoor microalgae-based urban wastewater treatment: recent advances, applications, and future perspectives. Wiley Interdiscip. Rev. Water (2021). https:\/\/doi.org\/10.1002\/wat2.1518","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"2711_CR29","doi-asserted-by":"publisher","first-page":"123046","DOI":"10.1016\/j.biortech.2020.123046","volume":"305","author":"S Rossi","year":"2020","unstructured":"Rossi, S., D\u00edez-Montero, R., Rueda, E., Cascino, F.C., Parati, K., Garc\u00eda, J., Ficara, E.: Free ammonia inhibition in microalgae and cyanobacteria grown in wastewaters: Photo-respirometric evaluation and modelling. Bioresour. Technol. 305, 123046 (2020). https:\/\/doi.org\/10.1016\/j.biortech.2020.123046","journal-title":"Bioresour. Technol."},{"key":"2711_CR30","doi-asserted-by":"publisher","DOI":"10.6041\/j.issn.1000-1298.2023.01.027","author":"J Chen","year":"2023","unstructured":"Chen, J., Ding, Q., Liu, L., Hou, L., Liu, Y., Shen, M.: Early detection of broilers respiratory diseases based on YOLO v5 and short time tracking. Trans. Chin. Soc. Agric. Mach. (2023). https:\/\/doi.org\/10.6041\/j.issn.1000-1298.2023.01.027","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"2711_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94463-0","volume-title":"Neural Networks and Deep Learning: A Textbook","author":"CC Aggarwal","year":"2018","unstructured":"Aggarwal, C.C.: Neural Networks and Deep Learning: A Textbook. Springer International Publishing, Cham (2018)"},{"key":"2711_CR32","doi-asserted-by":"publisher","first-page":"169","DOI":"10.11975\/j.issn.1002-6819.2022.04.020","volume":"38","author":"H Peng","year":"2022","unstructured":"Peng, H., Ji, Li., Xu, H., Chen, H., Xing, Z., He, H., Juntao, X.: Litchi detection based on multiple feature enhancement and feature fusion SSD. Trans. CSAE 38, 169\u2013177 (2022). https:\/\/doi.org\/10.11975\/j.issn.1002-6819.2022.04.020","journal-title":"Trans. CSAE"},{"key":"2711_CR33","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s12145-021-00703-5","volume":"15","author":"A Hachemi","year":"2022","unstructured":"Hachemi, A., Zeroual, A.: Computer-assisted program for water Calco-Carbonic equilibrium computation. Earth Sci Inform. 15, 689\u2013704 (2022). https:\/\/doi.org\/10.1007\/s12145-021-00703-5","journal-title":"Earth Sci Inform."},{"key":"2711_CR34","doi-asserted-by":"publisher","first-page":"108336","DOI":"10.1016\/j.cpc.2022.108336","volume":"276","author":"TEC Magalh\u00e3es","year":"2022","unstructured":"Magalh\u00e3es, T.E.C., Rebord\u00e3o, J.M.: PyWolf: A PyOpenCL implementation for simulating the propagation of partially coherent light. Comput. Phys. Commun. 276, 108336 (2022). https:\/\/doi.org\/10.1016\/j.cpc.2022.108336","journal-title":"Comput. Phys. Commun."},{"key":"2711_CR35","doi-asserted-by":"publisher","first-page":"3742","DOI":"10.1039\/D1LC00532D","volume":"21","author":"J Shen","year":"2021","unstructured":"Shen, J., Zheng, J., Li, Z., Liu, Y., Jing, F., Wan, X., Yamaguchi, Y., Zhuang, S.: A rapid nucleic acid concentration measurement system with large field of view for a droplet digital PCR microfluidic chip. Lab Chip. 21, 3742\u20133747 (2021). https:\/\/doi.org\/10.1039\/D1LC00532D","journal-title":"Lab Chip."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02711-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02711-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02711-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T15:34:22Z","timestamp":1706196862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02711-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,9]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2711"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02711-y","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,9]]},"assertion":[{"value":"3 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the authors have no competing interests or other interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}