{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T08:19:37Z","timestamp":1767860377118,"version":"3.49.0"},"reference-count":68,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"North-Caucasus Federal University"},{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"crossref","award":["23-71-10013"],"award-info":[{"award-number":["23-71-10013"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Food shortages are becoming increasingly urgent due to the growing global population. Enhancing oil crop yields, particularly sunflowers, is key to ensuring food security and the sustainable provision of vegetable fats essential for human nutrition and animal feed. However, sunflower yields are often reduced by diseases, pests, and other factors. Remote sensing technologies, such as unmanned aerial vehicle (UAV) scans and satellite monitoring, combined with machine learning algorithms, provide powerful tools for monitoring crop health, diagnosing diseases, mapping fields, and forecasting yields. These technologies enhance agricultural efficiency and reduce environmental impact, supporting sustainable development in agriculture. This systematic review aims to assess the accuracy of various machine learning technologies, including classification and segmentation algorithms, convolutional neural networks, random forests, and support vector machines. These methods are applied to monitor sunflower crop conditions, diagnose diseases, and forecast yields. It provides a comprehensive analysis of current methods and their potential for precision farming applications. The review also discusses future research directions, including the development of automated systems for crop monitoring and disease diagnostics.<\/jats:p>","DOI":"10.3390\/make7040130","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T09:16:07Z","timestamp":1761642967000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Systematic Review of Methods and Algorithms for the Intelligent Processing of Agricultural Data Applied to Sunflower Crops"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4783-0656","authenticated-orcid":false,"given":"Valentina","family":"Arustamyan","sequence":"first","affiliation":[{"name":"North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355029 Stavropol, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0487-4779","authenticated-orcid":false,"given":"Pavel","family":"Lyakhov","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modeling, North-Caucasus Federal University, 355029 Stavropol, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2949-7036","authenticated-orcid":false,"given":"Ulyana","family":"Lyakhova","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modeling, North-Caucasus Federal University, 355029 Stavropol, Russia"}]},{"given":"Ruslan","family":"Abdulkadirov","sequence":"additional","affiliation":[{"name":"North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355029 Stavropol, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6515-0224","authenticated-orcid":false,"given":"Vyacheslav","family":"Rybin","sequence":"additional","affiliation":[{"name":"Computer-Aided Design Department, Saint Petersburg State Electrotechnical University \u201cLETI\u201d, 197022 Saint Petersburg, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8941-4220","authenticated-orcid":false,"given":"Denis","family":"Butusov","sequence":"additional","affiliation":[{"name":"Computer-Aided Design Department, Saint Petersburg State Electrotechnical University \u201cLETI\u201d, 197022 Saint Petersburg, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"ref_1","unstructured":"(2024, January 18). Population|United Nations. Available online: https:\/\/www.un.org\/en\/global-issues\/population."},{"key":"ref_2","unstructured":"(2025, February 03). Hunger Numbers Stubbornly High for Three Consecutive Years as Global Crises Deepen: UN Report. Available online: https:\/\/www.who.int\/news\/item\/24-07-2024-hunger-numbers-stubbornly-high-for-three-consecutive-years-as-global-crises-deepen--un-report."},{"key":"ref_3","unstructured":"FAO (2023). The State of Food Security and Nutrition in the World, Food and Agriculture Organization of the United Nations."},{"key":"ref_4","unstructured":"FAO (2022). The Plants That Feed the World: Baseline Data and Metrics to Inform Strategies for the Conservation and Use of Plant Genetic Resources for Food and Agriculture, Food and Agriculture Organization of the United Nations."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4666","DOI":"10.1002\/fsn3.1783","article-title":"Oilseed Crop Sunflower (Helianthus annuus) as a Source of Food: Nutritional and Health Benefits","volume":"8","author":"Adeleke","year":"2020","journal-title":"Food Sci. Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Singh, H., Kukreja, V., Mehta, S., Gupta, S., and Aeri, M. (2024, January 24\u201326). AI in Agriculture: Federated Learning CNNs for Sunflower Leaf Disease Diagnosis. Proceedings of the 2024 5th International Conference for Emerging Technology, INCET, Belgaum, India.","DOI":"10.1109\/INCET61516.2024.10593449"},{"key":"ref_7","first-page":"30","article-title":"Rice Plant Diseases Detection Using Convolutional Neural Networks","volume":"14","author":"Agrawal","year":"2023","journal-title":"Int. J. Eng. Syst. Model. Simul."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"214305","DOI":"10.1016\/j.ccr.2021.214305","article-title":"Portable Electrochemical Sensing Methodologies for On-Site Detection of Pesticide Residues in Fruits and Vegetables","volume":"453","author":"Umapathi","year":"2022","journal-title":"Coord. Chem. Rev."},{"key":"ref_9","first-page":"19131","article-title":"Contributions of Artificial Intelligence for Circular Economy Transition Leading toward Sustainability: An Explorative Study in Agriculture and Food Industries of Pakistan","volume":"26","author":"Ali","year":"2024","journal-title":"Environ. Dev. Sustain. A Multidiscip. Approach Theory Pract. Sustain. Dev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109097","DOI":"10.1016\/j.compag.2024.109097","article-title":"A Systematic Review on Precision Agriculture Applied to Sunflowers, the Role of Hyperspectral Imaging","volume":"222","author":"Centorame","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sehgal, S., and Roy, A. (2022, January 4\u20135). A Study of Deep Learning Techniques on Oilseed Crops. Proceedings of the 3rd IEEE 2022 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS, Greater Noida, India.","DOI":"10.1109\/ICCCIS56430.2022.10037740"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Mondal, A.K., Chatterjee, S., Masud, M., Meshref, H., and Bairagi, A.K. (2023). Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI. Mathematics, 11.","DOI":"10.3390\/math11102241"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105812","DOI":"10.1016\/j.compag.2020.105812","article-title":"Identifying Sunflower Lodging Based on Image Fusion and Deep Semantic Segmentation with UAV Remote Sensing Imaging","volume":"179","author":"Song","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115762","DOI":"10.1016\/j.indcrop.2022.115762","article-title":"Prediction of Sunflower Grain Yield under Normal and Salinity Stress by RBF, MLP and, CNN Models","volume":"189","author":"Khalifani","year":"2022","journal-title":"Ind. Crop. Prod."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114132","DOI":"10.1016\/j.rse.2024.114132","article-title":"A Generalized Model for Mapping Sunflower Areas Using Sentinel-1 SAR Data","volume":"306","author":"Qadir","year":"2024","journal-title":"Remote. Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1109\/JSTARS.2023.3239756","article-title":"Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms","volume":"16","author":"Farmonov","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"127332","DOI":"10.1016\/j.eja.2024.127332","article-title":"Sunflower-YOLO: Detection of Sunflower Capitula in UAV Remote Sensing Images","volume":"160","author":"Jing","year":"2024","journal-title":"Eur. J. Agron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103861","DOI":"10.1016\/j.robot.2021.103861","article-title":"Multi-Spectral Image Synthesis for Crop\/Weed Segmentation in Precision Farming","volume":"146","author":"Fawakherji","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Poleshchenko, D., Mikhailov, I., and Petrov, V. (2023, January 29\u201331). On the Segmentation of Sunflower Plants in UAV Photos. Proceedings of the 2023 25th International Conference on Digital Signal Processing and its Applications, DSPA, Moscow, Russia.","DOI":"10.1109\/DSPA57594.2023.10113424"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_21","unstructured":"Kitchenham, B., and Charters, S.M. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering, Version 2.3, Keele University."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1111\/mpp.12164","article-title":"The Sunflower Downy Mildew Pathogen Plasmopara halstedii","volume":"16","author":"Gascuel","year":"2015","journal-title":"Mol. Plant Pathol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Banerjee, D., Sharma, N., Upadhyay, D., Singh, M., and Chythanya, K.R. (2024, January 24\u201326). Decoding Sunflower Downy Mildew: Leveraging Hybrid Deep Learning for Scale Severity Analysis. Proceedings of the 2024 5th International Conference for Emerging Technology, INCET, Belgaum, India.","DOI":"10.1109\/INCET61516.2024.10592879"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103733","DOI":"10.1016\/j.micron.2024.103733","article-title":"Light and Electron Microscopy of the Micromorphology and Development of Pycniospores and Aeciospores of the Sunflower Rust, Puccinia helianthi","volume":"189","author":"Baka","year":"2025","journal-title":"Micron"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102527","DOI":"10.1016\/j.pmpp.2024.102527","article-title":"Population Frequency Distribution and Introgression of Alternaria Species Causing Leaf Blight of Sunflower, India","volume":"136","author":"Shree","year":"2025","journal-title":"Physiol. Mol. Plant Pathol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Montecchia, J.F., Fass, M.I., Cerrudo, I., Quiroz, F.J., Nicosia, S., Maringolo, C.A., Di Rienzo, J., Troglia, C., Hopp, H.E., and Escande, A. (2021). On-Field Phenotypic Evaluation of Sunflower Populations for Broad-Spectrum Resistance to Verticillium Leaf Mottle and Wilt. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-91034-4"},{"key":"ref_27","unstructured":"Kandel, H., Endres, G., and Buetow, R. (2020). Sunflower Production Guide, North Dakota Agricultural Experiment Station and North Dakota State University Extension."},{"key":"ref_28","unstructured":"Hughes, D., and Salathe, M. (2015). An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"64476","DOI":"10.1109\/ACCESS.2023.3284680","article-title":"Pathogen-Based Classification of Plant Diseases: A Deep Transfer Learning Approach for Intelligent Support Systems","volume":"11","author":"Gowrishankar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gulzar, Y., \u00dcnal, Z., Akta\u015f, H., and Mir, M.S. (2023). Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study. Agriculture, 13.","DOI":"10.3390\/agriculture13081479"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108481","DOI":"10.1016\/j.compag.2023.108481","article-title":"DFN-PSAN: Multi-Level Deep Information Feature Fusion Extraction Network for Interpretable Plant Disease Classification","volume":"216","author":"Dai","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","first-page":"600","article-title":"Segmentation of Sunflower Leaf Disease Using Improved YOLO Network with IDMO Model","volume":"12","author":"Thilagavathi","year":"2024","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sathi, T.A., Hasan, M.A., and Alam, M.J. (2023, January 11\u201314). SunNet: A Deep Learning Approach to Detect Sunflower Disease. Proceedings of the 7th International Conference on Trends in Electronics and Informatics, ICOEI, Tirunelveli, India.","DOI":"10.1109\/ICOEI56765.2023.10125676"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sohel, A., Alam Sarker, M.M., Das, U.C., Das, P.K., Siddiquee, S.M.T., Haider Noori, S.R., and Thien Le, N. (2023, January 13\u201315). Sunflower Disease Identification Using Deep Learning: A Data-Driven Approach. Proceedings of the 2023 26th International Conference on Computer and Information Technology, ICCIT, Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ICCIT60459.2023.10441285"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Banerjee, D., Kukreja, V., Vats, S., Jain, V., and Goyal, B. (2023, January 6\u20138). AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines. Proceedings of the 2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC, Coimbatore, India.","DOI":"10.1109\/ICESC57686.2023.10193473"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Parez, S., Dilshad, N., and Lee, J.W. (2025). A Channel Attention-Driven Optimized CNN for Efficient Early Detection of Plant Diseases in Resource Constrained Environment. Agriculture, 15.","DOI":"10.3390\/agriculture15020127"},{"key":"ref_37","first-page":"15","article-title":"Ensemble of Visual Transformer and Deep Neural Networks for Recognizing Sunflower Diseases from Photographs","volume":"Volume 1023","author":"Baboshina","year":"2024","journal-title":"Lecture Notes in Networks and Systems, Proceedings of the NIELIT\u2019s International Conference on Communication, Electronics and Digital Technology, Guwahati, India, 6\u201317 February 2024"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108043","DOI":"10.1016\/j.dib.2022.108043","article-title":"An Extensive Sunflower Dataset Representation for Successful Identification and Classification of Sunflower Diseases","volume":"42","author":"Sara","year":"2022","journal-title":"Data Brief"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109431","DOI":"10.1016\/j.compag.2024.109431","article-title":"Advanced Image Segmentation for Precision Agriculture Using CNN-GAT Fusion and Fuzzy C-Means Clustering","volume":"226","author":"Peng","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e19507","DOI":"10.1016\/j.heliyon.2023.e19507","article-title":"Understanding the Spatio-Temporal Behaviour of the Sunflower Crop for Subfield Areas Delineation Using Sentinel-2 NDVI Time-Series Images in an Organic Farming System","volume":"9","author":"Marino","year":"2023","journal-title":"Heliyon"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Maleki, S., Baghdadi, N., Bazzi, H., Dantas, C.F., Ienco, D., Nasrallah, Y., and Najem, S. (2024). Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images. Remote Sens., 16.","DOI":"10.3390\/rs16234548"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Athar, U., Ali, M., Zafar, Z., Khurshid, H., Berns, K., and Fraz, M.M. (2024, January 9\u201310). Merging UAV-Derived Metrics with Crop Physiology to Estimate Sunflower Yield. Proceedings of the 2024 International Conference on Frontiers of Information Technology, FIT, Islamabad, Pakistan.","DOI":"10.1109\/FIT63703.2024.10838400"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100139","DOI":"10.1016\/j.srs.2024.100139","article-title":"Estimation of Sunflower Planted Areas in Ukraine during Full-Scale Russian Invasion: Insights from Sentinel-1 SAR Data","volume":"10","author":"Qadir","year":"2024","journal-title":"Sci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"100098","DOI":"10.1016\/j.atech.2022.100098","article-title":"Time-Series Analysis of Sentinel-2 Satellite Images for Sunflower Yield Estimation","volume":"3","author":"Amankulova","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2197509","DOI":"10.1080\/10106049.2023.2197509","article-title":"Sunflower Crop Yield Prediction by Advanced Statistical Modeling Using Satellite-Derived Vegetation Indices and Crop Phenology","volume":"38","author":"Amankulova","year":"2023","journal-title":"Geocarto Int."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Baup, F., Fieuzal, R., Ygorra, B., Gorrab, A., Riazanof, S., Martin-Comte, A., Gross, K., and Frappart, F. (2024, January 7\u201312). A 6-Year Analysis of Sentinel-1, Sentinel-2 and Landsat-8 Over Sunflower Crops and an Experimental Field in Southwestern France. Proceedings of the IGARSS 2024\u20142024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10640785"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ygorra, B., Baup, F., Fieuzal, R., Martin-Comte, A., Gross, K., Riazanoff, S., Frappart, F., and Wigneron, J.-P. (2024, January 7\u201312). CuSum-Nrt as a Crop Monitoring System: A Sentinel-1 Application to Sunflower and Sorghum in Southwestern France. Proceedings of the IGARSS 2024\u20142024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10640953"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, G., Han, W., Huang, S., Ma, W., Ma, Q., and Cui, X. (2021). Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13142721"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Poleshchenko, D., Petrov, V., and Mikhailov, I. (2022, January 9\u201311). Detection of Sunflower Plants in UAV Photos. Proceedings of the 2022 4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA, Lipetsk, Russia.","DOI":"10.1109\/SUMMA57301.2022.9974021"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sharma, H., Kukreja, V., Mehta, S., Nisha Chandran, S., and Garg, A. (2024, January 24\u201326). Plant AI in Agriculture: Innovative Approaches to Sunflower Leaf Disease Detection with Federated Learning CNNs. Proceedings of the 2024 5th International Conference for Emerging Technology, INCET, Belgaum, India.","DOI":"10.1109\/INCET61516.2024.10592966"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Joshi, J., Aeri, M., Kukreja, V., and Mehta, S. (2024, January 14\u201315). Revolutionizing in Agriculture: Federated CNN Models for Sunflower Leaf Diseases. Proceedings of the 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO, Noida, India.","DOI":"10.1109\/ICRITO61523.2024.10522441"},{"key":"ref_52","unstructured":"and Singh, A. (2024, January 18\u201320). Sunflower Sentry: Advanced Hybrid Model for Early Disease Detection. Proceedings of the 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Abuhani, D.A., Hussain, M.H., Khan, J., Elmohandes, M., and Zualkernan, I. (2023, January 23\u201325). Crop and Weed Detection in Sunflower and Sugarbeet Fields Using Single Shot Detectors. Proceedings of the 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS, Berlin, Germany.","DOI":"10.1109\/COINS57856.2023.10189257"},{"key":"ref_54","first-page":"100323","article-title":"ResNet Interpretation Methods Applied to the Classification of Foliar Diseases in Sunflower","volume":"9","author":"Dawod","year":"2022","journal-title":"J. Agric. Food Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rana, G., Singh, R., Pal, A., and Gupta, R. (2023, January 29\u201331). Enhancing Sunflower Disease Identification with CNN-SVM Integration. Proceedings of the 2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON, Bangalore, India.","DOI":"10.1109\/SMARTGENCON60755.2023.10442779"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sirohi, A., and Malik, A. (2021, January 28\u201330). A Hybrid Model for the Classification of Sunflower Diseases Using Deep Learning. Proceedings of the 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, UK.","DOI":"10.1109\/ICIEM51511.2021.9445342"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9211700","DOI":"10.1155\/2022\/9211700","article-title":"Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach","volume":"2022","author":"Malik","year":"2022","journal-title":"J. Food Qual."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dawod, R.G., and Dobre, C. (2021, January 26\u201328). Classification of Sunflower Foliar Diseases Using Convolutional Neural Network. Proceedings of the 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS, Bucharest, Romania.","DOI":"10.1109\/CSCS52396.2021.00084"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_60","unstructured":"Rajbongshi, A., Biswas, A.A., Biswas, J., Shakil, R., Akter, B., and Barman, M.R. (October, January 30). Sunflower Diseases Recognition Using Computer Vision-Based Approach. Proceedings of the IEEE Region 10 Humanitarian Technology Conference, R10-HTC, Bangalore, India."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Suryavanshi, A., Mehta, S., Malhotra, S., Choudhary, A., and Jain, V. (2024, January 26\u201327). Growing Insights: Federated Learning CNN\u2019s in Combatting Sunflower Leaf Diseases. Proceedings of the 2024 Asia Pacific Conference on Innovation in Technology (APCIT), Mysore, India.","DOI":"10.1109\/APCIT62007.2024.10673672"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.matpr.2021.05.182","article-title":"Optimization Classification of Sunflower Recognition through Machine Learning","volume":"51","author":"Kaur","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, S., Lv, F., and Huo, P. (2021, January 6\u20138). Improved Detection of Yolov4 Sunflower Leaf Diseases. Proceedings of the 2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China.","DOI":"10.1109\/ISCEIC53685.2021.00019"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hermawan, D.R., Fahrio Ghanial Fatihah, M., Kurniawati, L., and Helen, A. (2021, January 27\u201329). Comparative Study of J48 Decision Tree Classification Algorithm, Random Tree, and Random Forest on In-Vehicle CouponRecommendation Data. Proceedings of the 2021 International Conference on Artificial Intelligence and Big Data Analytics, Bandung, Indonesia.","DOI":"10.1109\/ICAIBDA53487.2021.9689701"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106881","DOI":"10.1016\/j.compag.2022.106881","article-title":"Hyperspectral Imaging Facilitates Early Detection of Orobanche Cumana Below-Ground Parasitism on Sunflower under Field Conditions","volume":"196","author":"Atsmon","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"435","DOI":"10.18287\/2412-6179-CO-1514","article-title":"Bidirectional Encoder Representation from Image Transformers for Recognizing Sunflower Diseases from Photographs","volume":"49","author":"Baboshina","year":"2025","journal-title":"Comput. Opt."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Tomassini, S., Ali Akber Dewan, M., Liaqat Ali, M., and Zhang, Z. (2024). The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection. Computers, 13.","DOI":"10.3390\/computers13120336"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Rani, L., Veeramanickam, M.R.M., and Pandey, B. (2024, January 3\u20134). Innovative Fusion for Sunflower Leaf Disease Identification: CNN and Random Forest Strategies. Proceedings of the 2024 IEEE AITU: Digital Generation, Conference Proceedings\u2014AITU, Astana, Kazakhstan.","DOI":"10.1109\/IEEECONF61558.2024.10585376"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T05:40:18Z","timestamp":1761716418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":68,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040130"],"URL":"https:\/\/doi.org\/10.3390\/make7040130","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,27]]}}}