{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T13:21:24Z","timestamp":1793020884768,"version":"4.5.0"},"reference-count":29,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification.<\/jats:p>","DOI":"10.3390\/jimaging10120327","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T11:17:22Z","timestamp":1734520642000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-5874","authenticated-orcid":false,"given":"Kacoutchy Jean","family":"Ayikpa","sequence":"first","affiliation":[{"name":"Laboratoire Imagerie et Vision Artificielle (ImVia), Universit\u00e9 de Bourgogne, 21000 Dijon, France"},{"name":"Unit\u00e9 de Recherche et d\u2019Expertise Num\u00e9rique (UREN), Universit\u00e9 Virtuelle de C\u00f4te d\u2019Ivoire, Abidjan 28 BP 536, C\u00f4te d\u2019Ivoire"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abou Bakary","family":"Ballo","sequence":"additional","affiliation":[{"name":"Laboratoire de M\u00e9canique et Information (LaMI), Universit\u00e9 Felix Houphou\u00ebt-Boigny, Abidjan 22 BP 801, C\u00f4te d\u2019Ivoire"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diarra","family":"Mamadou","sequence":"additional","affiliation":[{"name":"Laboratoire Imagerie et Vision Artificielle (ImVia), Universit\u00e9 de Bourgogne, 21000 Dijon, France"},{"name":"Laboratoire de M\u00e9canique et Information (LaMI), Universit\u00e9 Felix Houphou\u00ebt-Boigny, Abidjan 22 BP 801, C\u00f4te d\u2019Ivoire"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Gouton","sequence":"additional","affiliation":[{"name":"Laboratoire Imagerie et Vision Artificielle (ImVia), Universit\u00e9 de Bourgogne, 21000 Dijon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"ref_1","unstructured":"End, M.J., and Dand, R. (2015). F\u00e8ves de Cacao: Exigences de Qualit\u00e9 de l\u2019industrie du Chocolat et du Cacao, FCC."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"375","DOI":"10.17306\/J.JARD.2022.01646","article-title":"Market Analysis on Cocoa Beans Export: The Case of Ghana and Cote d\u2019Ivoire in West Africa","volume":"66","author":"Gyan","year":"2022","journal-title":"J. Agribus. Rural Dev."},{"key":"ref_3","first-page":"1025","article-title":"Relative Export Competitiveness of the Nigerian Cocoa Industry","volume":"32","author":"Abdullahi","year":"2022","journal-title":"Compet. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Marty-Terrade, S., and Marangoni, A.G. (2012). Impact of Cocoa Butter Origin on Crystal Behavior. Cocoa Butter and Related Compounds, Elsevier.","DOI":"10.1016\/B978-0-9830791-2-5.50014-1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chong, P.A., Newman, D.J., and Steinmacher, D.A. (2020). Somatic Embryogenesis for Clonal Propagation and Associated Molecular Studies in Cacao (Theobroma cacao L.). Agricultural, Forestry and Bioindustry Biotechnology and Biodiscovery, Springer International Publishing.","DOI":"10.1007\/978-3-030-51358-0"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Adhitya, Y., Prakosa, S.W., K\u00f6ppen, M., and Leu, J.-S. (2020). Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application. Agronomy, 10.","DOI":"10.3390\/agronomy10111642"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1007\/s11947-019-02350-7","article-title":"Laser-Based Imaging for Cocoa Pods Maturity Detection","volume":"12","author":"Lockman","year":"2019","journal-title":"Food Bioprocess. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6663455","DOI":"10.1155\/2021\/6663455","article-title":"Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method","volume":"2021","author":"Gao","year":"2021","journal-title":"Complexity"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"228","DOI":"10.14710\/jtsiskom.2020.13733","article-title":"Maturity Classification of Cacao through Spectrogram and Convolutional Neural Network","volume":"8","author":"Bueno","year":"2020","journal-title":"J. Teknol. Dan Sist. Komput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Galindo, J.A.M., Rosal, J.E.C., and Villaverde, J.F. (2022, January 13\u201315). Ripeness Classification of Cacao Using Cepstral-Based Statistical Features and Support Vector Machine. Proceedings of the 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia.","DOI":"10.1109\/IICAIET55139.2022.9936807"},{"key":"ref_11","first-page":"1","article-title":"Early Cocoa Blackpod Pathogen Prediction with Machine Learning Ensemble Algorithm Based on Climatic Parameters","volume":"46","year":"2022","journal-title":"J. Inf. Organ. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ayubi, A., Faiz, M., Situmorang, G.B., Ramadhani, K.N., and Utama, N.P. (2023, January 7\u20139). A Cocoa Ripeness Detection and Classification Model Based on Improved YOLOv5s. Proceedings of the 2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA), Lombok, Indonesia.","DOI":"10.1109\/ICAICTA59291.2023.10390119"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"de Oliveira, J.R.C.P., and Romero, R.A.P.F. (2018, January 8\u201313). Transfer Learning Based Model for Classification of Cocoa Pods. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489126"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109196","DOI":"10.1016\/j.dib.2023.109196","article-title":"CocoaMFDB: A Dataset of Cocoa Pod Maturity and Families in an Uncontrolled Environment in C\u00f4te d\u2019Ivoire","volume":"48","author":"Ayikpa","year":"2023","journal-title":"Data Brief"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.procs.2015.07.362","article-title":"Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space","volume":"57","author":"Shaik","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ayikpa, K.J., Mamadou, D., Gouton, P., and Adou, K.J. (2023). Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces. Data, 8.","DOI":"10.3390\/data8060099"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123244","DOI":"10.1016\/j.talanta.2022.123244","article-title":"RGB Color Sensor for Colorimetric Determinations: Evaluation and Quantitative Analysis of Colored Liquid Samples","volume":"241","author":"Machado","year":"2022","journal-title":"Talanta"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gen\u00e9-Mola, J., Llorens, J., Rosell-Polo, J.R., Gregorio, E., Arn\u00f3, J., Solanelles, F., Mart\u00ednez-Casasnovas, J.A., and Escol\u00e0, A. (2020). Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions. Sensors, 20.","DOI":"10.3390\/s20247072"},{"key":"ref_19","first-page":"1501","article-title":"Automatic Gray Images Colorization Based on Lab Color Space","volume":"18","author":"Abbadi","year":"2020","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_20","first-page":"1377","article-title":"Analysis of the Impact of Color Spaces on Skin Cancer Diagnosis Using Deep Learning Techniques","volume":"37","author":"Mamadou","year":"2023","journal-title":"Rev. D\u2019Intell. Artif."},{"key":"ref_21","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer Science and Business Media."},{"key":"ref_22","unstructured":"(2024, November 17). Introducing Data Science Big Data Machine Learning and More Using Python Tools|Super Adaptoid-Academia.Edu. Available online: https:\/\/www.academia.edu\/43139523\/Introducing_Data_Science_Big_Data_Machine_Learning_and_more_using_Python_tools."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sharma, M., Sharma, S., and Singh, G. (2018). Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining. Data, 3.","DOI":"10.3390\/data3040054"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Can, R., Kocaman, S., and Gokceoglu, C. (2021). A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey. Appl. Sci., 11.","DOI":"10.3390\/app11114993"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Khan, F., Ayoub, S., Gulzar, Y., Majid, M., Reegu, F.A., Mir, M.S., Soomro, A.B., and Elwasila, O. (2023). MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor. J. Imaging, 9.","DOI":"10.3390\/jimaging9080163"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Malvasi, A., Malgieri, L.E., Cicinelli, E., Vimercati, A., Achiron, R., Spari\u0107, R., D\u2019Amato, A., Baldini, G.M., Dellino, M., and Trojano, G. (2024). AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree. J. Imaging, 10.","DOI":"10.3390\/jimaging10080194"},{"key":"ref_27","first-page":"326","article-title":"The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting","volume":"14","author":"Khairy","year":"2021","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Khuriwal, N., and Mishra, N. (2018, January 13\u201314). Breast Cancer Diagnosis Using Adaptive Voting Ensemble Machine Learning Algorithm. Proceedings of the 2018 IEEMA Engineer Infinite Conference (eTechNxT), New Delhi, India.","DOI":"10.1109\/ETECHNXT.2018.8385355"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"135279","DOI":"10.1016\/j.jclepro.2022.135279","article-title":"Prediction of Compressive Strength of Rice Husk Ash Concrete Based on Stacking Ensemble Learning Model","volume":"382","author":"Li","year":"2023","journal-title":"J. Clean. Prod."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/12\/327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:02Z","timestamp":1760115302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/12\/327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":29,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["jimaging10120327"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10120327","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]}}}