{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T22:47:26Z","timestamp":1766962046024,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Humanities and Social Sciences Planning Fund Projects of Ministry of Education of China","award":["23YJAZH226"],"award-info":[{"award-number":["23YJAZH226"]}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"crossref","award":["2024JJ5042","2023JJ30050"],"award-info":[{"award-number":["2024JJ5042","2023JJ30050"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11554-024-01529-2","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T09:02:11Z","timestamp":1723107731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["YOLO-LF: a lightweight multi-scale feature fusion algorithm for wheat spike detection"],"prefix":"10.1007","volume":"21","author":[{"given":"Shuren","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Shengzhen","family":"Long","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"1529_CR1","doi-asserted-by":"publisher","first-page":"821717","DOI":"10.3389\/fpls.2022.821717","volume":"13","author":"C Wen","year":"2022","unstructured":"Wen, C., Wu, J., Chen, H., Su, H., Chen, X., Li, Z., Yang, C.: Wheat spike detection and counting in the field based on SpikeRetinaNet. Front. Plant Sci. 13, 821717 (2022)","journal-title":"Front. Plant Sci."},{"issue":"20","key":"1529_CR2","doi-asserted-by":"publisher","first-page":"17539","DOI":"10.1007\/s00521-022-07392-1","volume":"34","author":"T Alkhudaydi","year":"2022","unstructured":"Alkhudaydi, T., Lglesia, B.: Counting spikelets from infield wheat crop images using fully convolutional networks. Neural Comput. Appl. 34(20), 17539\u201317560 (2022)","journal-title":"Neural Comput. Appl."},{"key":"1529_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2352\/EI.2022.34.6.IRIACV-264","volume":"34","author":"E Ullah","year":"2022","unstructured":"Ullah, E., Ullah, M., Sajjad, M., Cheikh, F.A.: Deep learning based wheat ears count in robot images for wheat phenotyping. Electron. Imaging 34, 1\u20136 (2022)","journal-title":"Electron. Imaging"},{"key":"1529_CR4","doi-asserted-by":"publisher","first-page":"107161","DOI":"10.1016\/j.compag.2022.107161","volume":"199","author":"S Dandrifosse","year":"2022","unstructured":"Dandrifosse, S., Ennadifi, E., Carlier, A., Gosselin, B., Dumont, B., Mercatoris, B.: Deep learning for wheat ear segmentation and ear density measurement: from heading to maturity. Comput. Electron. Agric. 199, 107161 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"1529_CR5","doi-asserted-by":"publisher","first-page":"107623","DOI":"10.1016\/j.compag.2023.107623","volume":"205","author":"A Zaji","year":"2023","unstructured":"Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J.S., Ruan, Y.: AutoOLA: automatic object level augmentation for wheat spikes counting. Comput. Electron. Agric. 205, 107623 (2023)","journal-title":"Comput. Electron. Agric."},{"issue":"4","key":"1529_CR6","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1111\/tpj.14799","volume":"103","author":"JA Fernandez-Gallego","year":"2020","unstructured":"Fernandez-Gallego, J.A., Lootens, P., Borra-Serrano, I., Derycke, V., Haesaert, G., Rold\u00e1n-Ruiz, I., Araus, J.L., Kefauver, S.C.: Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant J. 103(4), 1603\u20131613 (2020)","journal-title":"Plant J."},{"key":"1529_CR7","doi-asserted-by":"publisher","first-page":"107439","DOI":"10.1016\/j.compag.2022.107439","volume":"203","author":"A Zaji","year":"2022","unstructured":"Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J.S., Ruan, Y.: Wheat spike localization and counting via hybrid UNet architectures. Comput. Electron. Agric. 203, 107439 (2022)","journal-title":"Comput. Electron. Agric."},{"issue":"11","key":"1529_CR8","first-page":"97","volume":"42","author":"L Dong","year":"2021","unstructured":"Dong, L., Guangqiao, C., Yibai, L., Cong, C.: Recognition and counting of wheat ears at flowering stage of heading poplar based on color features. J. Chin. Agric. Mech. 42(11), 97 (2021)","journal-title":"J. Chin. Agric. Mech."},{"issue":"1","key":"1529_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/s13007-023-01062-6","volume":"19","author":"X Xu","year":"2023","unstructured":"Xu, X., Geng, Q., Gao, F., Xiong, D., Qiao, H., Ma, X.: Segmentation and counting of wheat spike grains based on deep learning and textural feature. Plant Methods 19(1), 77 (2023)","journal-title":"Plant Methods"},{"key":"1529_CR10","doi-asserted-by":"crossref","unstructured":"Li, H., Di, L., Zhang, C., Lin, L., Guo, L.: Improvement of in-season crop mapping for Illinois cropland using multiple machine learning classifiers. In: 2022 10th International Conference on Agro-geoinformatics (Agro-Geoinformatics), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/Agro-Geoinformatics55649.2022.9859153"},{"issue":"2","key":"1529_CR11","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1080\/07038992.2021.1906213","volume":"47","author":"F Fourati","year":"2021","unstructured":"Fourati, F., Mseddi, W.S., Attia, R.: Wheat head detection using deep, semi-supervised and ensemble learning. Can. J. Remote. Sens. 47(2), 198\u2013208 (2021)","journal-title":"Can. J. Remote. Sens."},{"key":"1529_CR12","doi-asserted-by":"crossref","unstructured":"Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R.: WheatNet-lite: a novel light weight network for wheat head detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1332\u20131341 (2021)","DOI":"10.1109\/ICCVW54120.2021.00154"},{"issue":"1","key":"1529_CR13","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/s41597-022-01169-w","volume":"9","author":"L Lin","year":"2022","unstructured":"Lin, L., Di, L., Zhang, C., Guo, L., Di, Y., Li, H., Yang, A.: Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. Sci. Data 9(1), 63 (2022)","journal-title":"Sci. Data"},{"key":"1529_CR14","doi-asserted-by":"publisher","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","volume":"236","author":"M Weiss","year":"2020","unstructured":"Weiss, M., Jacob, F., Duveiller, G.: Remote sensing for agricultural applications: a meta-review. Remote Sens. Environ. 236, 111402 (2020)","journal-title":"Remote Sens. Environ."},{"key":"1529_CR15","unstructured":"Guo, H.: Wheat head counting by estimating a density map with convolutional neural networks. arXiv preprint (2023). arXiv:2303.10542"},{"key":"1529_CR16","doi-asserted-by":"publisher","first-page":"107087","DOI":"10.1016\/j.compag.2022.107087","volume":"198","author":"J Zhao","year":"2022","unstructured":"Zhao, J., Yan, J., Xue, T., Wang, S., Qiu, X., Yao, X., Tian, Y., Zhu, Y., Cao, W., Zhang, X.: A deep learning method for oriented and small wheat spike detection (OSWSDET) in UAV images. Comput. Electron. Agric. 198, 107087 (2022)","journal-title":"Comput. Electron. Agric."},{"issue":"5","key":"1529_CR17","first-page":"281","volume":"20","author":"K Laabassi","year":"2021","unstructured":"Laabassi, K., Belarbi, M.A., Mahmoudi, S., Mahmoudi, S.A., Ferhat, K.: Wheat varieties identification based on a deep learning approach. J. Saudi Soc. Agric. Sci. 20(5), 281\u2013289 (2021)","journal-title":"J. Saudi Soc. Agric. Sci."},{"key":"1529_CR18","doi-asserted-by":"publisher","first-page":"76235","DOI":"10.1109\/ACCESS.2021.3080836","volume":"9","author":"T Misra","year":"2021","unstructured":"Misra, T., Arora, A., Marwaha, S., Jha, R.R., Ray, M., Jain, R., Rao, A., Varghese, E., Kumar, S., Kumar, S., et al.: Web-SpikeSegNet: deep learning framework for recognition and counting of spikes from visual images of wheat plants. IEEE Access 9, 76235\u201376247 (2021)","journal-title":"IEEE Access"},{"key":"1529_CR19","doi-asserted-by":"publisher","first-page":"872555","DOI":"10.3389\/fpls.2022.872555","volume":"13","author":"R Qiu","year":"2022","unstructured":"Qiu, R., He, Y., Zhang, M.: Automatic detection and counting of wheat spikelet using semi-automatic labeling and deep learning. Front. Plant Sci. 13, 872555 (2022)","journal-title":"Front. Plant Sci."},{"key":"1529_CR20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1529_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1529_CR22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1529_CR23","doi-asserted-by":"publisher","first-page":"189043","DOI":"10.1109\/ACCESS.2020.3031896","volume":"8","author":"M-X He","year":"2020","unstructured":"He, M.-X., Hao, P., Xin, Y.-Z.: A robust method for wheatear detection using UAV in natural scenes. IEEE Access 8, 189043\u2013189053 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"1529_CR24","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s13007-023-00985-4","volume":"19","author":"S Xiang","year":"2023","unstructured":"Xiang, S., Wang, S., Xu, M., Wang, W., Liu, W.: YOLO POD: a fast and accurate multi-task model for dense soybean POD counting. Plant Methods 19(1), 8 (2023)","journal-title":"Plant Methods"},{"key":"1529_CR25","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neucom.2022.03.017","volume":"489","author":"S Khaki","year":"2022","unstructured":"Khaki, S., Safaei, N., Pham, H., Wang, L.: WheatNet: a lightweight convolutional neural network for high-throughput image-based wheat head detection and counting. Neurocomputing 489, 78\u201389 (2022)","journal-title":"Neurocomputing"},{"issue":"1","key":"1529_CR26","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1186\/s13007-023-01079-x","volume":"19","author":"J Ye","year":"2023","unstructured":"Ye, J., Yu, Z., Wang, Y., Lu, D., Zhou, H.: WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network. Plant Methods 19(1), 103 (2023)","journal-title":"Plant Methods"},{"key":"1529_CR27","doi-asserted-by":"crossref","unstructured":"Sangeetha, J., Govindarajan, P.: Prediction of agricultural waste compost maturity using fast regions with convolutional neural network (R-CNN). Mater. Today Proc. (2023)","DOI":"10.1016\/j.matpr.2023.01.112"},{"key":"1529_CR28","doi-asserted-by":"publisher","first-page":"834938","DOI":"10.3389\/fpls.2022.834938","volume":"13","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Min, A., Steffenson, B.J., Su, W.-H., Hirsch, C.D., Anderson, J., Wei, J., Ma, Q., Yang, C.: Wheat-Net: an automatic dense wheat spike segmentation method based on an optimized hybrid task cascade model. Front. Plant Sci. 13, 834938 (2022)","journal-title":"Front. Plant Sci."},{"issue":"5","key":"1529_CR29","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1016\/j.cj.2022.07.007","volume":"10","author":"L Li","year":"2022","unstructured":"Li, L., Hassan, M.A., Yang, S., Jing, F., Yang, M., Rasheed, A., Wang, J., Xia, X., He, Z., Xiao, Y.: Development of image-based wheat spike counter through a faster R-CNN algorithm and application for genetic studies. Crop J. 10(5), 1303\u20131311 (2022)","journal-title":"Crop J."},{"key":"1529_CR30","doi-asserted-by":"crossref","unstructured":"Im\u00a0Choi, J., Tian, Q.: Visual-saliency-guided channel pruning for deep visual detectors in autonomous driving. In: 2023 IEEE Intelligent Vehicles Symposium (IV), pp. 1\u20136. IEEE (2023)","DOI":"10.1109\/IV55152.2023.10186819"},{"key":"1529_CR31","first-page":"1","volume":"60","author":"D Wang","year":"2021","unstructured":"Wang, D., Zhang, D., Yang, G., Xu, B., Luo, Y., Yang, X.: SSRNet: in-field counting wheat ears using multi-stage convolutional neural network. IEEE Trans. Geosci. Remote Sens. 60, 1\u201311 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1529_CR32","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28. (2015)"},{"key":"1529_CR33","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint (2017). arXiv:1704.04861"},{"key":"1529_CR34","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1529_CR35","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al.: Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1529_CR36","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"1529_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1529_CR38","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116\u2013131 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"1529_CR39","unstructured":"Mehta, S., Rastegari, M.: MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint (2021). arXiv:2110.02178"},{"key":"1529_CR40","doi-asserted-by":"crossref","unstructured":"Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., Yan, S.: MetaFormer is actually what you need for vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10819\u201310829 (2022)","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"1529_CR41","doi-asserted-by":"crossref","unstructured":"Maaz, M., Shaker, A., Cholakkal, H., Khan, S., Zamir, S.W., Anwer, R.M., Shahbaz\u00a0Khan, F.: EdgeNext: efficiently amalgamated CNN-transformer architecture for mobile vision applications. In: European Conference on Computer Vision, pp. 3\u201320. Springer (2022)","DOI":"10.1007\/978-3-031-25082-8_1"},{"key":"1529_CR42","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H., Chan, S.-H.G.: Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021\u201312031 (2023)","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"1529_CR43","unstructured":"Wang, A., Chen, H., Lin, Z., Pu, H., Ding, G.: RepViT: revisiting mobile CNN from vit perspective. arXiv preprint (2023). arXiv:2307.09283"},{"key":"1529_CR44","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1529_CR45","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOV7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1529_CR46","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25. (2012)"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01529-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01529-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01529-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T16:04:32Z","timestamp":1724774672000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01529-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1529"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01529-2","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"23 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":3,"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 no competing interests. Financial and personal relationships: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"148"}}