{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T10:53:07Z","timestamp":1768128787348,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,5]],"date-time":"2023-03-05T00:00:00Z","timestamp":1677974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapidly changing climate affects an extensive spectrum of human-centered environments. The food industry is one of the affected industries due to rapid climate change. Rice is a staple food and an important cultural key point for Japanese people. As Japan is a country in which natural disasters continuously occur, using aged seeds for cultivation has become a regular practice. It is a well-known truth that seed quality and age highly impact germination rate and successful cultivation. However, a considerable research gap exists in the identification of seeds according to age. Hence, this study aims to implement a machine-learning model to identify Japanese rice seeds according to their age. Since agewise datasets are unavailable in the literature, this research implements a novel rice seed dataset with six rice varieties and three age variations. The rice seed dataset was created using a combination of RGB images. Image features were extracted using six feature descriptors. The proposed algorithm used in this study is called Cascaded-ANFIS. A novel structure for this algorithm is proposed in this work, combining several gradient-boosting algorithms such as XGBoost, CatBoost, and LightGBM. The classification was conducted in two steps. First, the seed variety was identified. Then, the age was predicted. As a result, seven classification models were implemented. The performance of the proposed algorithm was evaluated against 13 state-of-the-art algorithms. Overall, the proposed algorithm has a higher accuracy, precision, recall, and F1-score than the others. For the classification of variety, the proposed algorithm scored 0.7697, 0.7949, 0.7707, and 0.7862, respectively. The results of this study confirm that the proposed algorithm can be employed in the successful age classification of seeds.<\/jats:p>","DOI":"10.3390\/s23052828","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T02:28:34Z","timestamp":1678069714000},"page":"2828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Age Classification of Rice Seeds in Japan Using Gradient-Boosting and ANFIS Algorithms"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5235-8552","authenticated-orcid":false,"given":"Namal","family":"Rathnayake","sequence":"first","affiliation":[{"name":"School of Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akira","family":"Miyazaki","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Kochi University, Kochi 780-8072, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9966-5576","authenticated-orcid":false,"given":"Tuan Linh","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Information and Communications Technology, Hanoi University of Science and Technology, No. 1, Dai Co Viet Road, Hanoi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5285-8007","authenticated-orcid":false,"given":"Yukinobu","family":"Hoshino","sequence":"additional","affiliation":[{"name":"School of Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,5]]},"reference":[{"key":"ref_1","unstructured":"Yamaji, S., and It\u00f4, S. (2019). Japanese and American Agriculture, Routledge."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10333-009-0187-5","article-title":"The effects of irrigation method, age of seedling and spacing on crop performance, productivity and water-wise rice production in Japan","volume":"8","author":"Chapagain","year":"2010","journal-title":"Paddy Water Environ."},{"key":"ref_3","first-page":"279","article-title":"Effects of site of origin, time of seed maturation, and seed age on germination behavior of Portulaca oleracea from the Old and New Worlds","volume":"78","year":"2000","journal-title":"Can. J. Bot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.2135\/cropsci1987.0011183X002700050046x","article-title":"Seed age and salt tolerance at germination in alfalfa 1","volume":"27","author":"Smith","year":"1987","journal-title":"Crop. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1023\/A:1013257407909","article-title":"Seed aging, delayed germination and reduced competitive ability in Bromus tectorum","volume":"155","author":"Rice","year":"2001","journal-title":"Plant Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"576","DOI":"10.2134\/agronj1926.00021962001800070003x","article-title":"Germination of rice seed as affected by temperature, fungicides, and age","volume":"18","author":"Jones","year":"1926","journal-title":"Agron. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3923\/rjes.2009.184.192","article-title":"The Effect of Seed Aging on the Seedling Growth as Affected by","volume":"3","author":"Soltani","year":"2009","journal-title":"Res. J. Environ. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.4141\/cjps-2014-021","article-title":"Seedling age and quality upon transplanting affect seed yield of canola (Brassica napus L.)","volume":"94","author":"Ren","year":"2014","journal-title":"Can. J. Plant Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.5897\/AJAR12.1839","article-title":"Effects of seed dormancy level and storage container on seed longevity and seedling vigour of jute mallow (Corchorus olitorius)","volume":"8","author":"Ibrahim","year":"2013","journal-title":"Afr. J. Agric. Res."},{"key":"ref_10","first-page":"62","article-title":"The changes of germination characteristics and enzyme activity of barley seeds under accelerated aging","volume":"48","author":"Tabatabaei","year":"2015","journal-title":"Cercet. Agron. Mold"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"93","DOI":"10.18178\/joig.4.2.93-98","article-title":"Improving leaf classification rate via background removal and ROI extraction","volume":"4","author":"Wu","year":"2016","journal-title":"J. Image Graph."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.tifs.2011.01.008","article-title":"Recent advances in the use of computer vision technology in the quality assessment of fresh meats","volume":"22","author":"Jackman","year":"2011","journal-title":"Trends Food Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1049\/iet-ipr.2018.5106","article-title":"Use of hyperspectral imaging for cake moisture and hardness prediction","volume":"13","author":"Polak","year":"2019","journal-title":"IET Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10812-015-0076-1","article-title":"Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions","volume":"82","author":"Qiao","year":"2015","journal-title":"J. Appl. Spectrosc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kelman, T., Ren, J., and Marshall, S. (2013). Effective classification of Chinese tea samples in hyperspectral imaging. Artif. Intell. Res., 2.","DOI":"10.5430\/air.v2n4p87"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22493","DOI":"10.1109\/ACCESS.2020.2969847","article-title":"Varietal classification of rice seeds using RGB and hyperspectral images","volume":"8","author":"Fabiyi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","first-page":"168","article-title":"Application of pattern recognition techniques in the analysis of cereal grains","volume":"63","author":"Lai","year":"1986","journal-title":"Cereal Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/0260-8774(95)00022-4","article-title":"Two-dimensional image analysis of the shape of rice and its application to separating varieties","volume":"27","author":"Sakai","year":"1996","journal-title":"J. Food Eng."},{"key":"ref_19","unstructured":"Hong, P.T.T., Hai, T.T.T., Hoang, V.T., Hai, V., and Nguyen, T.T. (2015, January 8\u201310). Comparative study on vision based rice seed varieties identification. Proceedings of the 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), Ho Chi Minh City, Vietnam."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1631\/jzus.2005.B1095","article-title":"Identification of rice seed varieties using neural network","volume":"6","author":"Liu","year":"2005","journal-title":"J. Zhejiang-Univ.-Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Peralta, C.N.M., Pabico, J.P., and Mariano, V.Y. (2016, January 27\u201329). Modeling shapes using uniform cubic b-splines for rice seed image analysis. Proceedings of the 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), Ha-Long, Vietnam.","DOI":"10.1109\/CCE.2016.7562657"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, K.Y., and Chien, M.C. (2017). A novel method of identifying paddy seed varieties. Sensors, 17.","DOI":"10.3390\/s17040809"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.compag.2016.07.020","article-title":"Identifying rice grains using image analysis and sparse-representation-based classification","volume":"127","author":"Kuo","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s12161-014-9916-5","article-title":"Use of hyperspectral imaging to discriminate the variety and quality of rice","volume":"8","author":"Wang","year":"2015","journal-title":"Food Anal. Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1007\/s11947-009-0267-y","article-title":"Quantification of nitrogen status in rice by least squares support vector machines and reflectance spectroscopy","volume":"5","author":"Shao","year":"2012","journal-title":"Food Bioprocess Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8916","DOI":"10.3390\/s130708916","article-title":"Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis","volume":"13","author":"Kong","year":"2013","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vu, H., Tachtatzis, C., Murray, P., Harle, D., Dao, T.K., Le, T.L., Andonovic, I., and Marshall, S. (2016, January 7\u20139). Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection. Proceedings of the 2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Hanoi, Vietnam.","DOI":"10.1109\/RIVF.2016.7800289"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12297","DOI":"10.1111\/jfpe.12297","article-title":"A method for rapid identification of rice origin by hyperspectral imaging technology","volume":"40","author":"Sun","year":"2017","journal-title":"J. Food Process. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1109\/TCSVT.2002.800512","article-title":"An efficient algorithm for video sequence matching using the modified Hausdorff distance and the directed divergence","volume":"12","author":"Kim","year":"2002","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rathnayake, N., Rathnayake, U., Dang, T.L., and Hoshino, Y. (2022). An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm. Sensors, 22.","DOI":"10.3390\/s22124401"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Park, D.K., Jeon, Y.S., and Won, C.S. (November, January 30). Efficient use of local edge histogram descriptor. Proceedings of the 2000 ACM workshops on Multimedia, Los Angeles, CA, USA.","DOI":"10.1145\/357744.357758"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Somnugpong, S., and Khiewwan, K. (2016, January 13\u201315). Content-based image retrieval using a combination of color correlograms and edge direction histogram. Proceedings of the 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), Khon Kaen, Thailand.","DOI":"10.1109\/JCSSE.2016.7748911"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1049\/iet-ipr.2011.0445","article-title":"Image retrieval and classification using adaptive local binary patterns based on texture features","volume":"6","author":"Lin","year":"2012","journal-title":"IET Image Process."},{"key":"ref_34","first-page":"1","article-title":"GLCM texture: A tutorial","volume":"3","year":"2000","journal-title":"Natl. Counc. Geogr. Inf. Anal. Remote Sens. Core Curric"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_38","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Adv. Neural Inf. Process. Syst., 31."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_40","unstructured":"Meng, Q., Ke, G., Wang, T., Chen, W., Ye, Q., Ma, Z.M., and Liu, T.Y. (2016). A communication-efficient parallel algorithm for decision tree. Adv. Neural Inf. Process. Syst., 29."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jin, R., and Agrawal, G. (2003, January 1\u20133). Communication and memory efficient parallel decision tree construction. Proceedings of the 2003 SIAM International Conference on Data Mining, San Francisco, CA, USA.","DOI":"10.1137\/1.9781611972733.11"},{"key":"ref_42","unstructured":"Ranka, S., and Singh, V. (1998, January 27\u201321). CLOUDS: A decision tree classifier for large datasets. Proceedings of the 4th Knowledge Discovery And Data Mining Conference, New York, NY, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ins.2004.02.015","article-title":"Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system","volume":"170","author":"Melin","year":"2005","journal-title":"Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1007\/s40815-021-01076-z","article-title":"A novel optimization algorithm: Cascaded adaptive neuro-fuzzy inference system","volume":"23","author":"Rathnayake","year":"2021","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rathnayake, N., Rathnayake, U., Dang, T.L., and Hoshino, Y. (2022). A Cascaded Adaptive Network-Based Fuzzy Inference System for Hydropower Forecasting. Sensors, 22.","DOI":"10.3390\/s22082905"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2828\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:48:10Z","timestamp":1760122090000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,5]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052828"],"URL":"https:\/\/doi.org\/10.3390\/s23052828","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,5]]}}}