{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:35:44Z","timestamp":1768282544064,"version":"3.49.0"},"reference-count":129,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Machine learning and deep learning techniques integrated with advanced sensing technologies have revolutionized agricultural engineering, addressing complex challenges in food production, quality assessment, and environmental monitoring. This survey presents a systematic review and meta-analysis of recent developments by examining the peer-reviewed literature from 2015 to 2024. The analysis reveals computational approaches ranging from traditional algorithms like support vector machines and random forests to deep learning architectures, including convolutional and recurrent neural networks. Deep learning models often demonstrate superior performance, showing 5\u201310% accuracy improvements over traditional methods and achieving 93\u201399% accuracy in image-based applications. Three primary application domains are identified: agricultural product quality assessment using hyperspectral imaging, crop and field management through precision optimization, and agricultural automation with machine vision systems. Dataset taxonomy shows spectral data predominating at 42.1%, followed by image data at 26.2%, indicating preference for non-destructive approaches. Current challenges include data limitations, model interpretability issues, and computational complexity. Future trends emphasize lightweight model development, ensemble learning, and expanding applications. This analysis provides a comprehensive understanding of current capabilities and future directions for machine learning in agricultural engineering, supporting the development of efficient and sustainable agricultural systems for global food security.<\/jats:p>","DOI":"10.3390\/computers14100438","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:12:29Z","timestamp":1760523149000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine and Deep Learning in Agricultural Engineering: A Comprehensive Survey and Meta-Analysis of Techniques, Applications, and Challenges"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3071-4704","authenticated-orcid":false,"given":"Samuel Akwasi","family":"Frimpong","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"},{"name":"Department of Computer Engineering, Ghana Communication Technology University, Accra PMB 100, Accra-North, Ghana"}]},{"given":"Mu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Wenyi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9653-1476","authenticated-orcid":false,"given":"Xiaowei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2540-3861","authenticated-orcid":false,"given":"Ernest","family":"Akpaku","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Ama Pokuah","family":"Obeng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kumasi Technical University, Kumasi P.O. 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Biotechnol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98492","DOI":"10.1109\/ACCESS.2024.3428401","article-title":"Emerging technologies for sustainable agriculture: The power of humans and the way ahead","volume":"12","author":"Louta","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"03003","DOI":"10.1051\/e3sconf\/202454203003","article-title":"Sustainable practices and Technological Innovations Transforming Agribusiness Dynamics","volume":"542","author":"Rushchitskaya","year":"2024","journal-title":"E3S Web Conf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, S.O., Peres, R.S., Ramalho, J.C., Lidon, F., and Barata, J. (2023). Machine learning applications in agriculture: Current trends, challenges, and future perspectives. Agronomy, 13.","DOI":"10.3390\/agronomy13122976"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, H., Xiong, S., Frimpong, S.A., and Zhang, M. (2020). A consortium blockchain-based agricultural machinery scheduling system. Sensors, 20.","DOI":"10.3390\/s20092643"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60155","DOI":"10.1109\/ACCESS.2024.3390581","article-title":"Evaluation of Machine Learning Approaches for precision farming in Smart Agriculture System: A comprehensive review","volume":"12","author":"Mohyuddin","year":"2024","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abubakar, R., Effah, E.K., Frimpong, S.A., Acakpovi, A., Acheampong, P., Kadambi, G.R., and Kumar, K.M.S. (2019, January 27\u201329). Adoption of smart grid in Ghana using Pattern Recognition Neural Networks. Proceedings of the 2019 International Conference on Computing, Computational Modelling and Applications (ICCMA), Cape Coast, Ghana.","DOI":"10.1109\/ICCMA.2019.00018"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/ACCESS.2020.3048415","article-title":"Machine learning applications for Precision Agriculture: A comprehensive review","volume":"9","author":"Sharma","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mes\u00edas-Ruiz, G.A., P\u00e9rez-Ortiz, M., Dorado, J., de Castro, A.I., and Pe\u00f1a, J.M. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1143326"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7693","DOI":"10.1111\/1750-3841.17373","article-title":"Artificial intelligence as a tool for predicting the quality attributes of garlic (Allium sativum L.) slices during continuous infrared-assisted hot air drying","volume":"89","author":"Qenawy","year":"2024","journal-title":"J. Food Sci."},{"key":"ref_11","first-page":"110","article-title":"Low-cost livestock sorting information management system based on deep learning","volume":"9","author":"Pan","year":"2023","journal-title":"Artif. Intell. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100483","DOI":"10.1016\/j.atech.2024.100483","article-title":"Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation","volume":"8","author":"Padhiary","year":"2024","journal-title":"Smart Agric. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2126734","DOI":"10.1155\/2024\/2126734","article-title":"Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review","volume":"2024","author":"Getahun","year":"2024","journal-title":"Sci. World J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Taha, M.F., Mao, H., Zhang, Z., Elmasry, G., Awad, M.A., Abdalla, A., Mousa, S., Elwakeel, A.E., and Elsherbiny, O. (2025). Emerging technologies for precision crop management towards agriculture 5.0: A comprehensive overview. Agriculture, 15.","DOI":"10.3390\/agriculture15060582"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qin, Y.-M., Tu, Y.-H., Li, T., Ni, Y., Wang, R.-F., and Wang, H. (2025). Deep Learning for Sustainable Agriculture: A systematic review on applications in lettuce cultivation. Sustainability, 17.","DOI":"10.3390\/su17073190"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s44279-024-00078-3","article-title":"Unlocking the potential of precision agriculture for Sustainable Farming","volume":"2","author":"Mgendi","year":"2024","journal-title":"Discov. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Fan, S., Zuo, M., Zhang, B., Zhu, Q., and Kong, J. (2024). Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning. Foods, 13.","DOI":"10.3390\/foods13101570"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1007\/s11119-024-10164-7","article-title":"Integrative approaches in modern agriculture: IOT, ML and AI for disease forecasting amidst climate change","volume":"25","author":"Delfani","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mienye, I.D., and Swart, T.G. (2024). A comprehensive review of deep learning: Architectures, recent advances, and applications. Information, 15.","DOI":"10.3390\/info15120755"},{"key":"ref_20","first-page":"101392","article-title":"Impact of crop management practices on maize yield: Insights from farming in tropical regions and predictive modeling using machine learning","volume":"18","author":"Bhat","year":"2024","journal-title":"J. Agric. Food Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ngugi, H.N., Akinyelu, A.A., and Ezugwu, A.E. (2024). Machine learning and deep learning for crop disease diagnosis: Performance analysis and review. Agronomy, 14.","DOI":"10.3390\/agronomy14123001"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Botero-Valencia, J., Garc\u00eda-Pineda, V., Valencia-Arias, A., Valencia, J., Reyes-Vera, E., Mejia-Herrera, M., and Hern\u00e1ndez-Garc\u00eda, R. (2025). Machine learning in sustainable agriculture: Systematic Review and research perspectives. Agriculture, 15.","DOI":"10.3390\/agriculture15040377"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ali, T., Rehman, S.U., Ali, S., Mahmood, K., Obregon, S.A., Iglesias, R.C., Khurshaid, T., and Ashraf, I. (2024). Smart agriculture: Utilizing machine learning and deep learning for drought stress identification in crops. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-74127-8"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"162799","DOI":"10.1109\/ACCESS.2024.3486653","article-title":"Crop classification and yield prediction using robust machine learning models for agricultural sustainability","volume":"12","author":"Badshah","year":"2024","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s00299-024-03294-9","article-title":"Advancing Plant Biology through deep learning-powered natural language processing","volume":"43","author":"Peng","year":"2024","journal-title":"Plant Cell Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"135251","DOI":"10.1016\/j.foodchem.2022.135251","article-title":"A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging","volume":"409","author":"Zhou","year":"2022","journal-title":"Food Chem."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Monteiro, A., Santos, S., and Gon\u00e7alves, P. (2021). Precision Agriculture for crop and livestock farming\u2014Brief review. Animals, 11.","DOI":"10.3390\/ani11082345"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13677-023-00583-8","article-title":"Challenges in remote sensing based climate and crop monitoring: Navigating the complexities using AI","volume":"13","author":"Han","year":"2024","journal-title":"J. Cloud Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Peladarinos, N., Piromalis, D., Cheimaras, V., Tserepas, E., Munteanu, R.A., and Papageorgas, P. (2023). Enhancing smart agriculture by implementing digital twins: A comprehensive review. Sensors, 23.","DOI":"10.3390\/s23167128"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2460098","DOI":"10.1155\/js\/2460098","article-title":"Smart sensor technologies shaping the future of precision agriculture: Recent advances and future outlooks","volume":"2025","author":"Aarif","year":"2025","journal-title":"J. Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3560261","article-title":"A survey of Computer Vision Technologies in urban and controlled-environment agriculture","volume":"56","author":"Luo","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Soussi, A., Zero, E., Sacile, R., Trinchero, D., and Fossa, M. (2024). Smart sensors and smart data for Precision Agriculture: A Review. Sensors, 24.","DOI":"10.3390\/s24082647"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"52815","DOI":"10.1109\/ACCESS.2024.3386552","article-title":"PAMICRM: Improving Precision Agriculture through multimodal image analysis for crop water requirement estimation using multidomain remote sensing data samples","volume":"12","author":"Munaganuri","year":"2024","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"136253","DOI":"10.1109\/ACCESS.2021.3116814","article-title":"AgriFusion: An architecture for IOT and emerging technologies based on a Precision Agriculture Survey","volume":"9","author":"Singh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qu, H.-R., and Su, W.-H. (2024). Deep learning-based weed\u2013crop recognition for smart agricultural equipment: A Review. Agronomy, 14.","DOI":"10.3390\/agronomy14020363"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abdulhussain, S.H., Mahmmod, B.M., Alwhelat, A., Shehada, D., Shihab, Z.I., Mohammed, H.J., Abdulameer, T.H., Alsabah, M., Fadel, M.H., and Ali, S.K. (2025). A comprehensive review of sensor technologies in IOT: Technical aspects, challenges, and future directions. Computers, 14.","DOI":"10.3390\/computers14080342"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alshuwaier, F.A., and Alsulaiman, F.A. (2025). Fake news detection using machine learning and Deep Learning Algorithms: A comprehensive review and future perspectives. Computers, 14.","DOI":"10.3390\/computers14090394"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1108\/IMDS-07-2019-0361","article-title":"Machine Learning facilitated business intelligence (part I)","volume":"120","author":"Khan","year":"2019","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102205","DOI":"10.1016\/j.aei.2023.102205","article-title":"Alada: A Lite Automatic Data Augmentation Framework for industrial defect detection","volume":"58","author":"Wang","year":"2023","journal-title":"Adv. Eng. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., \u0141obodzi\u0144ska, A., and \u015anieg, M. (2025). The IOT and AI in agriculture: The Time is now\u2014A systematic review of Smart Sensing Technologies. Sensors, 25.","DOI":"10.3390\/s25123583"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1109\/ACCESS.2024.3349418","article-title":"A comprehensive review on Deep Learning Assisted Computer Vision Techniques for smart greenhouse agriculture","volume":"12","author":"Akbar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gargon, E., Williamson, P.R., and Clarke, M. (2015). Collating the knowledge base for core outcome set development: Developing and appraising the search strategy for a systematic review. BMC Med Res. Methodol., 15.","DOI":"10.1186\/s12874-015-0019-9"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e1433","DOI":"10.1002\/cl2.1433","article-title":"Searching for studies: A guide to information retrieval for Campbell Systematic Reviews","volume":"20","author":"MacDonald","year":"2024","journal-title":"Campbell Syst. Rev."},{"key":"ref_44","first-page":"210","article-title":"Errors in search strategies used in systematic reviews and their effects on information retrieval","volume":"107","year":"2019","journal-title":"J. Med Libr. Assoc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.ject.2024.09.002","article-title":"Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and Sustainability","volume":"2","author":"Islam","year":"2024","journal-title":"J. Econ. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, S., Yang, Y., Yin, H., Zhao, J., Wang, T., Yang, X., Ren, J., and Yin, C. (2025). Towards digital transformation of agriculture for sustainable development in China: Experience and lessons learned. Sustainability, 17.","DOI":"10.3390\/su17083756"},{"key":"ref_47","first-page":"2168568","article-title":"An interdisciplinary approach to artificial intelligence in agriculture","volume":"95","author":"Ryan","year":"2023","journal-title":"NJAS Impact Agric. Life Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cogr.2023.04.001","article-title":"Artificial Intelligence, Machine Learning and deep learning in advanced robotics, a review","volume":"3","author":"Soori","year":"2023","journal-title":"Cogn. Robot."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1016\/j.ipm.2004.01.004","article-title":"Bibliographic database access using free-text and controlled vocabulary: An evaluation","volume":"41","author":"Savoy","year":"2005","journal-title":"Inf. Process. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cravero, A., Pardo, S., Sep\u00falveda, S., and Mu\u00f1oz, L. (2022). Challenges to use machine learning in Agricultural Big Data: A systematic literature review. Agronomy, 12.","DOI":"10.20944\/preprints202202.0345.v1"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"127610","DOI":"10.1016\/j.eja.2025.127610","article-title":"Integrating machine learning with agroecosystem modelling: Current State and future challenges","volume":"168","author":"Aderele","year":"2025","journal-title":"Eur. J. Agron."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yin, S., Xi, Y., Zhang, X., Sun, C., and Mao, Q. (2025). Foundation models in agriculture: A comprehensive review. Agriculture, 15.","DOI":"10.3390\/agriculture15080847"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"116280","DOI":"10.1016\/j.lwt.2024.116280","article-title":"Effects of storage conditions and packaging materials on the postharvest quality of fresh Chinese tomatoes and the optimization of the tomatoes\u2019 physiochemical properties using machine learning techniques","volume":"201","author":"Adelusi","year":"2024","journal-title":"LWT"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"109510","DOI":"10.1016\/j.lwt.2020.109510","article-title":"Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm","volume":"129","author":"Guo","year":"2020","journal-title":"LWT"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3278595","DOI":"10.1155\/2018\/3278595","article-title":"Artificial Neural Network Modeling of Drying Kinetics and Color Changes of Ginkgo Biloba Seeds during Microwave Drying Process","volume":"2018","author":"Bai","year":"2018","journal-title":"J. Food Qual."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.fbp.2024.04.003","article-title":"Predictive modeling of garlic quality in hybrid infrared-convective drying using artificial neural networks","volume":"145","author":"Qenawy","year":"2024","journal-title":"Food Bioprod. Process."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tong, Z., Zhang, S., Yu, J., Zhang, X., Wang, B., and Zheng, W. (2023). A Hybrid Prediction Model for CatBoost Tomato Transpiration Rate Based on Feature Extraction. Agronomy, 13.","DOI":"10.3390\/agronomy13092371"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhao, S., Jiao, T., Adade, S.Y.S.S., Wang, Z., Wu, X., Li, H., and Chen, Q. (2024). Based on vis-NIR combined with ANN for on-line detection of bacterial concentration during kombucha fermentation. Food Biosci., 60.","DOI":"10.1016\/j.fbio.2024.104346"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Huang, Y., Pan, Y., Liu, C., Zhou, L., Tang, L., Wei, H., Fan, K., Wang, A., and Tang, Y. (2024). Rapid and Non-Destructive Geographical Origin Identification of Chuanxiong Slices Using Near-Infrared Spectroscopy and Convolutional Neural Networks. Agriculture, 14.","DOI":"10.3390\/agriculture14081281"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6510","DOI":"10.1007\/s42729-023-01507-w","article-title":"Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques","volume":"23","author":"Li","year":"2023","journal-title":"J. Soil Sci. Plant Nutr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e13897","DOI":"10.1111\/jfpe.13897","article-title":"Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible-near infrared hyperspectral imaging","volume":"44","author":"Zhou","year":"2021","journal-title":"J. Food Process. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"110009","DOI":"10.1016\/j.foodcont.2023.110009","article-title":"Prediction of resilience and cohesion of deep-fried tofu by ultrasonic detection and LightGBM regression","volume":"154","author":"Xuan","year":"2023","journal-title":"Food Control."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.biosystemseng.2018.09.017","article-title":"Prediction of seed distribution in rectangular vibrating tray using grey model and artificial neural network","volume":"175","author":"Zhao","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yan, Y., Li, J., Chen, X., and Jiang, H. (2022). Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM. Foods, 11.","DOI":"10.3390\/foods11111658"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"112949","DOI":"10.1016\/j.scienta.2024.112949","article-title":"Optimal training strategy for high-performance detection model of multi-cultivar tea shoots based on deep learning methods","volume":"328","author":"Zhang","year":"2024","journal-title":"Sci. Hortic."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, Y., Zhao, Y., Pan, Q., Jin, K., Xu, G., and Hu, Y. (2023). TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model. Agronomy, 13.","DOI":"10.3390\/agronomy13051411"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e14238","DOI":"10.1111\/jfpp.14238","article-title":"Detection of viability of soybean seed based on fluorescence hyperspectra and CARS-SVM-AdaBoost model","volume":"43","author":"Li","year":"2019","journal-title":"J. Food Process. Preserv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e16414","DOI":"10.1111\/jfpp.16414","article-title":"Detection of soluble solid content in apples based on hyperspectral technology combined with deep learning algorithm","volume":"46","author":"Tian","year":"2022","journal-title":"J. Food Process. Preserv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Peng, Y., Zhao, S., and Liu, J. (2021). Fused deep features-based grape varieties identification using support vector machine. Agriculture, 11.","DOI":"10.3390\/agriculture11090869"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"136798","DOI":"10.1016\/j.foodchem.2023.136798","article-title":"Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea","volume":"428","author":"Li","year":"2023","journal-title":"Food Chem."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"108302","DOI":"10.1016\/j.agwat.2023.108302","article-title":"Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments","volume":"283","author":"Elbeltagi","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhu, J., Wu, B., Zhao, C., Sun, J., and Dai, C. (2019). Discrimination of Chinese liquors based on electronic nose and fuzzy discriminant principal component analysis. Foods, 8.","DOI":"10.3390\/foods8010038"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"135705","DOI":"10.1016\/j.foodchem.2023.135705","article-title":"Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models","volume":"414","author":"Zhu","year":"2023","journal-title":"Food Chem."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1002\/jsfa.12376","article-title":"Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves","volume":"103","author":"Cheng","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"137978","DOI":"10.1016\/j.foodchem.2023.137978","article-title":"Rapid determination of geographical authenticity and pungency intensity of the red Sichuan pepper (Zanthoxylum bungeanum) using differential pulse voltammetry and machine learning algorithms","volume":"439","author":"Zhang","year":"2023","journal-title":"Food Chem."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"141411","DOI":"10.1016\/j.foodchem.2024.141411","article-title":"Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production","volume":"463","author":"Zhao","year":"2024","journal-title":"Food Chem."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4403","DOI":"10.1111\/1750-3841.17151","article-title":"Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near-infrared hyperspectral imaging technology and machine learning algorithms","volume":"89","author":"Sun","year":"2024","journal-title":"J. Food Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"5486","DOI":"10.1002\/jsfa.13381","article-title":"Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning","volume":"104","author":"You","year":"2024","journal-title":"J. Sci. Food Agric."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Qiu, G., Lu, H., Wang, X., Wang, C., Xu, S., Liang, X., and Fan, C. (2023). Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies. Horticulturae, 9.","DOI":"10.3390\/horticulturae9080889"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"127297","DOI":"10.1016\/j.eja.2024.127297","article-title":"Improving carbon flux estimation in tea plantation ecosystems: A machine learning ensemble approach","volume":"160","author":"Raza","year":"2024","journal-title":"Eur. J. Agron."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e13797","DOI":"10.1111\/jfpe.13797","article-title":"A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology","volume":"44","author":"Sun","year":"2021","journal-title":"J. Food Process. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Qiu, D., Guo, T., Yu, S., Liu, W., Li, L., Sun, Z., Peng, H., and Hu, D. (2024). Classification of Apple Color and Deformity Using Machine Vision Combined with CNN. Agriculture, 14.","DOI":"10.3390\/agriculture14070978"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e13603","DOI":"10.1111\/jfpe.13603","article-title":"Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling-whale optimization algorithm-support vector machine","volume":"44","author":"Tang","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"112945","DOI":"10.1016\/j.scienta.2024.112945","article-title":"Individual nursery trees classification and segmentation using a point cloud-based neural network with dense connection pattern","volume":"328","author":"Xu","year":"2024","journal-title":"Sci. Hortic."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yu, Z., Guo, Y., Zhang, L., Ding, Y., Zhang, G., and Zhang, D. (2024). Improved Lightweight Zero-Reference Deep Curve Estimation Low-Light Enhancement Algorithm for Night-Time Cow Detection. Agriculture, 14.","DOI":"10.3390\/agriculture14071003"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Xue, Y., and Jiang, H. (2023). Monitoring of Chlorpyrifos Residues in Corn Oil Based on Raman Spectral Deep-Learning Model. Foods, 12.","DOI":"10.3390\/foods12122402"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/ijfs.16173","article-title":"Non-destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm","volume":"58","author":"Xu","year":"2023","journal-title":"Int. J. Food Sci. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"109194","DOI":"10.1016\/j.agwat.2024.109194","article-title":"Enhancing cotton irrigation with distributional actor\u2013critic reinforcement learning","volume":"307","author":"Chen","year":"2025","journal-title":"Agric. Water Manag."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, Z., Zhang, Y., Zhou, J., Wu, J., and Li, P. (2022). Real-time detection and location of potted flowers based on a ZED camera and a YOLO V4-tiny deep learning algorithm. Horticulturae, 8.","DOI":"10.3390\/horticulturae8010021"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.foodchem.2015.11.084","article-title":"Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms","volume":"197","author":"Khulal","year":"2016","journal-title":"Food Chem."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Chen, S., Qi, J., Gao, J., Chen, W., Fei, J., Meng, H., and Ma, Z. (2025). Research on the Control System for the Conveying and Separation Experimental Platform of Tiger Nut Harvester Based on Sensing Technology and Control Algorithms. Agriculture, 15.","DOI":"10.3390\/agriculture15010115"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e13432","DOI":"10.1111\/jfpe.13432","article-title":"Research on apple origin classification based on variable iterative space shrinkage approach with stepwise regression\u2013support vector machine algorithm and visible-near infrared hyperspectral imaging","volume":"43","author":"Tian","year":"2020","journal-title":"J. Food Process Eng."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TPAMI.2022.3145392","article-title":"Deep Roc analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation","volume":"45","author":"Carrington","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Chicco, D., T\u00f6tsch, N., and Jurman, G. (2021). The Matthews Correlation Coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, 14.","DOI":"10.1186\/s13040-021-00244-z"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"6790","DOI":"10.1002\/jsfa.12777","article-title":"Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness","volume":"103","author":"Liang","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1080\/02664760220136203","article-title":"Design optimization using ANOVA","volume":"29","author":"Hafeez","year":"2002","journal-title":"J. Appl. Stat."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e15447","DOI":"10.1111\/jfpp.15447","article-title":"Multivariate analysis of variance: An advanced chemometric approach to differentiate dose-dependent antioxidant activities of grape (Vitis labruscana) skin extracts","volume":"45","author":"Sridhar","year":"2021","journal-title":"J. Food Process. Preserv."},{"key":"ref_98","first-page":"194","article-title":"Real-time grain breakage sensing for rice combine harvesters using machine vision technology","volume":"13","author":"Chen","year":"2020","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_99","first-page":"3503","article-title":"Performance evaluation of latest meta-heuristic algorithms in finding optimum value of mathematical functions and problems","volume":"49","author":"Mirzakuchaki","year":"2024","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1093\/ce\/zkad066","article-title":"Parameter identification and generality analysis of photovoltaic module dual-diode model based on artificial hummingbird algorithm","volume":"7","author":"Li","year":"2023","journal-title":"Clean Energy"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"127828","DOI":"10.1016\/j.foodchem.2020.127828","article-title":"Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features","volume":"338","author":"Jiang","year":"2021","journal-title":"Food Chem."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"3203","DOI":"10.1108\/BFJ-12-2019-0941","article-title":"Feasibility study for the analysis of coconut water using fluorescence spectroscopy coupled with PARAFAC and SVM methods","volume":"122","author":"Gu","year":"2020","journal-title":"Br. Food J."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"e13631","DOI":"10.1111\/jfpe.13631","article-title":"Detection of browning of fresh-cut potato chips based on machine vision and electronic nose","volume":"44","author":"Hongyang","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_104","first-page":"158","article-title":"Preprocessing method of night vision image application in apple harvesting robot","volume":"11","author":"Jia","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"108186","DOI":"10.1016\/j.foodcont.2021.108186","article-title":"Rapid detection of chloramphenicol in food using SERS flexible sensor coupled artificial intelligent tools","volume":"128","author":"Li","year":"2021","journal-title":"Food Control"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1007\/s12161-017-1095-8","article-title":"Rapid Pseudomonas Species Identification from Chicken by Integrating Colorimetric Sensors with Near-Infrared Spectroscopy","volume":"11","author":"Xu","year":"2018","journal-title":"Food Anal. Methods"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.tifs.2018.09.001","article-title":"Colorimetric sensor arrays based on chemo-responsive dyes for food odor visualization","volume":"81","author":"Huang","year":"2018","journal-title":"Trends Food Sci. Technol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"4632","DOI":"10.1007\/s11947-024-03403-2","article-title":"Microwave Infrared Cooperative Drying of Ginger: Moisture Evolution, Structure Change, Physicochemical Properties, and Prediction Model","volume":"17","author":"Zeng","year":"2024","journal-title":"Food Bioprocess Technol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10462-024-11081-x","article-title":"Transfer learning in agriculture: A Review","volume":"58","author":"Hossen","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"100199","DOI":"10.1016\/j.grets.2025.100199","article-title":"Applications of machine learning and Deep Learning in agriculture: A comprehensive review","volume":"3","author":"Waqas","year":"2025","journal-title":"Green Technol. Sustain."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s44279-024-00066-7","article-title":"Transforming agriculture with machine learning, Deep Learning, and IOT: Perspectives from Ethiopia\u2014Challenges and opportunities","volume":"2","author":"Benti","year":"2024","journal-title":"Discov. Agric."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"121264","DOI":"10.1016\/j.jenvman.2024.121264","article-title":"A ga-stacking ensemble approach for forecasting energy consumption in a smart household: A Comparative Study of Ensemble Methods","volume":"364","author":"Dostmohammadi","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_113","unstructured":"Sankareswari, K., and Sujatha, G. (2023, January 23\u201325). Evaluation of an ensemble technique for prediction of crop yield. Proceedings of the 5th International Conference on Information Management & Machine Intelligence, Jaipur, India."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s12393-024-09395-1","article-title":"Artificial Intelligence in food manufacturing: A review of current work and future opportunities","volume":"17","author":"Canatan","year":"2025","journal-title":"Food Eng. Rev."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Arteaga-Cabrera, E., Ram\u00edrez-M\u00e1rquez, C., S\u00e1nchez-Ram\u00edrez, E., Segovia-Hern\u00e1ndez, J.G., Osorio-Mora, O., and G\u00f3mez-Salazar, J.A. (2025). Advancing optimization strategies in the food industry: From traditional approaches to multi-objective and technology-integrated solutions. Appl. Sci., 15.","DOI":"10.3390\/app15073846"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"e40836","DOI":"10.1016\/j.heliyon.2024.e40836","article-title":"Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and Sustainability","volume":"10","author":"Jabed","year":"2024","journal-title":"Heliyon"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"97567","DOI":"10.1109\/ACCESS.2025.3571340","article-title":"A federated explainable AI framework for Smart Agriculture: Enhancing Transparency, efficiency, and Sustainability","volume":"13","author":"Tahir","year":"2025","journal-title":"IEEE Access"},{"key":"ref_118","first-page":"257","article-title":"Explainable artificial intelligence and interpretable machine learning for Agricultural Data Analysis","volume":"6","author":"Ryo","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"107342","DOI":"10.1016\/j.rineng.2025.107342","article-title":"Edge-enabled Smart Agriculture Framework: Integrating IOT, Lightweight Deep Learning, and agentic AI for context-aware farming","volume":"28","author":"Tariq","year":"2025","journal-title":"Results Eng."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Gong, R., Zhang, H., Li, G., and He, J. (2025). Edge computing-enabled Smart Agriculture: Technical Architectures, practical evolution, and Bottleneck Breakthroughs. Sensors, 25.","DOI":"10.3390\/s25175302"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Zarbakhsh, S., Fakhrzad, F., Rajkovic, D., Niedba\u0142a, G., and Piekutowska, M. (2025). Approaches and challenges in machine learning for monitoring agricultural products and predicting plant physiological responses to biotic and abiotic stresses. Curr. Plant Biol., 43.","DOI":"10.1016\/j.cpb.2025.100535"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Mansoor, S., Iqbal, S., Popescu, S.M., Kim, S.L., Chung, Y.S., and Baek, J.-H. (2025). Integration of smart sensors and IOT in Precision Agriculture: Trends, challenges and future prospectives. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1587869"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Hackfort, S. (2021). Patterns of inequalities in digital agriculture: A systematic literature review. Sustainability, 13.","DOI":"10.3390\/su132212345"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Zhu, H., Liang, S., Lin, C., He, Y., and Xu, J.-L. (2024). Using multi-sensor data fusion techniques and machine learning algorithms for improving UAV-based yield prediction of oilseed rape. Drones, 8.","DOI":"10.3390\/drones8110642"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"108817","DOI":"10.1016\/j.compag.2024.108817","article-title":"Digitalization in agriculture. Towards an integrative approach","volume":"219","author":"Romera","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"100743","DOI":"10.1016\/j.jik.2025.100743","article-title":"Advancing Agricultural Economic Growth Through Technology Innovation and Structural Transformation: A multilevel analysis","volume":"10","author":"Li","year":"2025","journal-title":"J. Innov. Knowl."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1002\/sd.2773","article-title":"Artificial Intelligence\u2014Driven Sustainable Development: Examining Organizational, technical, and processing approaches to achieving Global Goals","volume":"32","author":"Kulkov","year":"2023","journal-title":"Sustain. Dev."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"2423249","DOI":"10.1080\/23311932.2024.2423249","article-title":"The role of agricultural extension services in promoting agricultural sustainability: A central malawi case study","volume":"10","author":"Mungai","year":"2024","journal-title":"Cogent Food Agric."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"100710","DOI":"10.1016\/j.atech.2024.100710","article-title":"Ethical, legal and social aspects (ELSA) for AI: An assessment tool for agri-food","volume":"10","author":"Ryan","year":"2025","journal-title":"Smart Agric. Technol."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/10\/438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T04:27:53Z","timestamp":1760675273000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/10\/438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":129,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["computers14100438"],"URL":"https:\/\/doi.org\/10.3390\/computers14100438","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}