{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:42:16Z","timestamp":1772829736720,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.<\/jats:p>","DOI":"10.3390\/e23060721","type":"journal-article","created":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T23:59:55Z","timestamp":1623023995000},"page":"721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5692-1242","authenticated-orcid":false,"given":"Ao","family":"Feng","sequence":"first","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxiang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjiang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6344-4053","authenticated-orcid":false,"given":"Haibo","family":"Pu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"ref_1","first-page":"9","article-title":"Closing yield and harvest area gaps to mitigate water scarcity related to China\u2019s rice production","volume":"245","author":"Kang","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_2","first-page":"315","article-title":"Principle Component Analysis for Classification of the Quality of Aromatic Rice","volume":"15","author":"Kartikadarma","year":"2017","journal-title":"Int. J. Comput. Ence Inf. Secur."},{"key":"ref_3","first-page":"14","article-title":"GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality","volume":"9","author":"Chen","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/S1671-2927(07)60066-2","article-title":"Correlation Analysis of Rice Seed Setting Rate and Weight of 1000-Grain and Agro-Meteorology over the Middle and Lower Reaches of the Yangtze River, China","volume":"6","author":"Zhao","year":"2007","journal-title":"Agric. Sci. China"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1016\/S2095-3119(19)62798-X","article-title":"Comparative analysis on grain quality and yield of different panicle weight indica-japonica hybrid rice (Oryza sativa L.) cultivars","volume":"19","author":"Bian","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3389\/fpls.2020.01120","article-title":"Forecasting Corn Yield with Machine Learning Ensembles","volume":"11","author":"Shahhosseini","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ngiap, H., and Watson, I. (1996, January 8\u201313). Optimising Image Processing Systems to Accurately Count Colony Forming Units. Proceedings of the European Meeting on Lasers and Electro-Optics, Hamburg, Germany.","DOI":"10.1364\/CLEO_EUROPE.1996.CThQ5"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hong, T.-P., Yang, Y.-C., Su, J.-H., and Wang, S.-L. (2019). Recognition and Counting of Motorcycles by Fusing Support Vector Machine and Deep Learning, Springer.","DOI":"10.1007\/978-981-13-9190-3_15"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13012-017-0641-5","article-title":"The Human Behaviour-Change Project: Harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation","volume":"12","author":"Michie","year":"2017","journal-title":"Implement. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1111\/bju.15035","article-title":"Deep learning computer vision algorithm for detecting kidney stone composition","volume":"125","author":"Black","year":"2020","journal-title":"BJU Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1049\/iet-ipr.2019.1067","article-title":"Segmentation techniques for early cancer detection in red blood cells with deep learning-based classifier-a comparative approach","volume":"14","author":"Shemona","year":"2020","journal-title":"IET Image Process"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1111\/jen.12834","article-title":"Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method","volume":"145","author":"Rustia","year":"2021","journal-title":"J. Appl. Entomol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/LGRS.2019.2954735","article-title":"A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests","volume":"17","author":"Tetila","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64177","DOI":"10.1109\/ACCESS.2019.2916931","article-title":"Soybean Seed Counting based on Pod Image using Two-column Convolution Neural Network","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jing, H., Peiyuan, L., and Hanwei, C. (2014, January 10\u201311). Research on the Rice Counting Method Based on Connected Component Labeling. Proceedings of the 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China.","DOI":"10.1109\/ICMTMA.2014.133"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., and Patel, V.M. (2017, January 22\u201329). Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.206"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ranjan, V., Le, H., and Hoai, M. (2018). Iterative Crowd Counting, Springer.","DOI":"10.1007\/978-3-030-01234-2_17"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.sigpro.2019.107270","article-title":"Gaussian kernel adaptive filters with adaptive kernel bandwidth","volume":"166","author":"Zhao","year":"2020","journal-title":"Signal Process"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1007\/s11263-019-01282-1","article-title":"Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks","volume":"128","author":"Tabernik","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","first-page":"346","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2014","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_23","unstructured":"Lempitsky, V., and Zisserman, A. (2021, January 6\u20139). Learning To count objects in images. Proceedings of the 24th Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Suneetha, K.R., and Krishnamoorti, R. (2010, January 5\u20137). Advanced Version of A Priori Algorithm. Proceedings of the 2010 First International Conference on Integrated Intelligent Computing, Bangalore, India.","DOI":"10.1109\/ICIIC.2010.64"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., and Ma, Y. (2016, January 27\u201330). Single-Image Crowd Counting via Multi-Column Convolutional Neural Network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q.V., Nguyen, P., Senior, A., Vanhoucke, V., and Dean, J. (2013, January 26\u201331). On rectified linear units for speech processing. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638312"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Science, T.D.J.C. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1109\/TIP.2018.2818439","article-title":"Learning-Based Just-Noticeable-Quantization- Distortion Modeling for Perceptual Video Coding","volume":"27","author":"Ki","year":"2018","journal-title":"IEEE Trans. Image Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/6\/721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:11:12Z","timestamp":1760163072000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/6\/721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,5]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["e23060721"],"URL":"https:\/\/doi.org\/10.3390\/e23060721","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,5]]}}}