{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:15:23Z","timestamp":1778310923823,"version":"3.51.4"},"reference-count":109,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T00:00:00Z","timestamp":1575676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSRF","award":["T1E\u0394K-00300"],"award-info":[{"award-number":["T1E\u0394K-00300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.<\/jats:p>","DOI":"10.3390\/jimaging5120089","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T05:54:51Z","timestamp":1575870891000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":249,"title":["Machine Vision Systems in Precision Agriculture for Crop Farming"],"prefix":"10.3390","volume":"5","author":[{"given":"Efthimia","family":"Mavridou","sequence":"first","affiliation":[{"name":"Human-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 57001 Thermi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0148-8592","authenticated-orcid":false,"given":"Eleni","family":"Vrochidou","sequence":"additional","affiliation":[{"name":"Human-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 57001 Thermi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5545-1499","authenticated-orcid":false,"given":"George A.","family":"Papakostas","sequence":"additional","affiliation":[{"name":"Human-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 57001 Thermi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6329-0533","authenticated-orcid":false,"given":"Theodore","family":"Pachidis","sequence":"additional","affiliation":[{"name":"Human-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 57001 Thermi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1639-0627","authenticated-orcid":false,"given":"Vassilis G.","family":"Kaburlasos","sequence":"additional","affiliation":[{"name":"Human-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 57001 Thermi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pereira, C.S., Morais, R., and Reis, M.J.C.S. (2017, January 7\u20138). Recent advances in image processing techniques for automated harvesting purposes: A review. Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), London, UK.","DOI":"10.1109\/IntelliSys.2017.8324352"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.compag.2018.07.032","article-title":"An automated detection and classification of citrus plant diseases using image processing techniques: A review","volume":"153","author":"Iqbal","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2018.08.001","article-title":"Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review","volume":"153","author":"Rieder","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pajares, G., Garc\u00eda-Santill\u00e1n, I., Campos, Y., Montalvo, M., Guerrero, J., Emmi, L., Romeo, J., Guijarro, M., and Gonzalez-de-Santos, P. (2016). Machine-Vision Systems Selection for Agricultural Vehicles: A Guide. J. Imaging, 2.","DOI":"10.3390\/jimaging2040034"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0168-1699(99)00061-7","article-title":"Agricultural automatic guidance research in North America","volume":"25","author":"Reid","year":"2000","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.biosystemseng.2016.06.014","article-title":"Agricultural robots for field operations: Concepts and components","volume":"149","author":"Bechar","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_8","unstructured":"Shalal, N., Low, T., McCarthy, C., and Hancock, N. (2013, January 22\u201325). A Review of Autonomous Navigation Systems in Agricultural Environments. Proceedings of the  2013 Society for Engineering in Agriculture Conference: Innovative Agricultural Technologies for a Sustainable Future, Barton, Australia."},{"key":"ref_9","first-page":"1","article-title":"Autonomous robots for agricultural tasks and farm assignment and future trends in agro robots","volume":"13","author":"Yaghoubi","year":"2013","journal-title":"Int. J. Mech. Mechatronics Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S0168-1699(99)00060-5","article-title":"Research in autonomous agriculture vehicles in Japan","volume":"25","author":"Torii","year":"2000","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ji, B., Zhu, W., Liu, B., Ma, C., and Li, X. (December, January 30). Review of Recent Machine-Vision Technologies in Agriculture. Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling, Wuhan, China.","DOI":"10.1109\/KAM.2009.231"},{"key":"ref_12","unstructured":"Kitchenham, B. (2004). Procedures for Performing Systematic Reviews, Computer Science Department, Keele University (TR\/SE0401) and National ICT Australia Ltd. (0400011T.1). Joint Technical Report."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.compedu.2011.10.006","article-title":"Exploring the educational potential of robotics in schools: A systematic review","volume":"58","author":"Benitti","year":"2012","journal-title":"Comput. Educ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Badeka, E., Kalabokas, T., Tziridis, K., Nicolaou, A., Vrochidou, E., Mavridou, E., Papakostas, G.A., and Pachidis, T. (2019). Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors. Computer Vision Systems (ICVS 2019), Springer.","DOI":"10.1007\/978-3-030-34995-0_9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compag.2017.08.025","article-title":"A robust algorithm based on color features for grape cluster segmentation","volume":"142","author":"Maleki","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","first-page":"192","article-title":"Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting","volume":"11","author":"Li","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.compind.2018.03.010","article-title":"Apple flower detection using deep convolutional networks","volume":"99","author":"Dias","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1754016","DOI":"10.1142\/S0218001417540167","article-title":"Cucumber Detection Based on Texture and Color in Greenhouse","volume":"31","author":"Li","year":"2017","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Prasetyo, E., Adityo, R.D., Suciati, N., and Fatichah, C. (2017, January 11\u201312). Mango leaf image segmentation on HSV and YCbCr color spaces using Otsu thresholding. Proceedings of the 2017 3rd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2017.8011860"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.biosystemseng.2017.06.021","article-title":"Machine vision system for the automatic segmentation of plants under different lighting conditions","volume":"161","author":"Sabzi","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1002\/jsfa.8080","article-title":"Computer vision-based method for classification of wheat grains using artificial neural network","volume":"97","author":"Sabanci","year":"2017","journal-title":"J. Sci. Food Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yahata, S., Onishi, T., Yamaguchi, K., Ozawa, S., Kitazono, J., Ohkawa, T., Yoshida, T., Murakami, N., and Tsuji, H. (2017, January 14\u201319). A hybrid machine learning approach to automatic plant phenotyping for smart agriculture. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966067"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/TPAMI.2008.275","article-title":"Faster and Better: A Machine Learning Approach to Corner Detection","volume":"32","author":"Rosten","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust Real-Time Face Detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K. (1994). Contrast Limited Adaptive Histogram Equalization. Graphics Gems, Elsevier.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"ref_28","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer. [1st ed.]. 2006. corr. 2nd Printing ed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1186\/s13007-017-0254-7","article-title":"Panicle-SEG: A robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization","volume":"13","author":"Xiong","year":"2017","journal-title":"Plant Methods"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, M.-Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the CVPR 2011, Providence, RI, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, W., Tang, W., Li, M., Zhang, D., and Zhang, Y. (2011, January 9\u201310). Corn tassel detection based on image processing. Proceedings of the 2012 International Workshop on Image Processing and Optical Engineering, SPIE, Harbin, China.","DOI":"10.1117\/12.917672"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.cj.2015.03.002","article-title":"Determination of rice panicle numbers during heading by multi-angle imaging","volume":"3","author":"Duan","year":"2015","journal-title":"Crop J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.biosystemseng.2016.04.007","article-title":"Region-based colour modelling for joint crop and maize tassel segmentation","volume":"147","author":"Lu","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1854011","DOI":"10.1142\/S0218001418540113","article-title":"Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm","volume":"32","author":"Zhang","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_36","first-page":"1","article-title":"Image segmentation of overlapping leaves based on Chan\u2013Vese model and Sobel operator","volume":"5","author":"Wang","year":"2018","journal-title":"Inf. Process. Agric."},{"key":"ref_37","first-page":"104","article-title":"Detecting Tomato Flowers in Greenhouses Using Computer Vision","volume":"11","author":"Oppenheim","year":"2017","journal-title":"Int. J. Comput. Electr. Autom. Control Inf. Eng."},{"key":"ref_38","first-page":"150","article-title":"Geometry-based mass grading of mango fruits using image processing","volume":"4","author":"Momin","year":"2017","journal-title":"Inf. Process. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Z., Walsh, K., and Verma, B. (2017). On-Tree Mango Fruit Size Estimation Using RGB-D Images. Sensors, 17.","DOI":"10.3390\/s17122738"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ponce, J.M., Aquino, A., Mill\u00e1n, B., and And\u00fajar, J.M. (2018). Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling. Sensors, 18.","DOI":"10.3390\/s18092930"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2018.01.011","article-title":"A methodology for fresh tomato maturity detection using computer vision","volume":"146","author":"Wan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9734\/AJOPACS\/2018\/39653","article-title":"Detection of Soya Beans Ripeness Using Image Processing Techniques and Artificial Neural Network","volume":"5","author":"Abdulhamid","year":"2018","journal-title":"Asian J. Phys. Chem. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Choi, D., Lee, W.S., Schueller, J.K., Ehsani, R., Roka, F., and Diamond, J. (2017, January 16\u201319). A performance comparison of RGB, NIR, and depth images in immature citrus detection using deep learning algorithms for yield prediction. Proceedings of the 2017 Spokane, Washington, DC, USA.","DOI":"10.13031\/aim.201700076"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sethy, P.K., Routray, B., and Behera, S.K. (2019). Detection and Counting of Marigold Flower Using Image Processing Technique. Lecture Notes in Networks and Systems, Springer.","DOI":"10.1007\/978-981-13-3122-0_9"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/LRA.2017.2651944","article-title":"Counting Apples and Oranges With Deep Learning: A Data-Driven Approach","volume":"2","author":"Chen","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2017.05.019","article-title":"An yield estimation in citrus orchards via fruit detection and counting using image processing","volume":"140","author":"Dorj","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cheng, H., Damerow, L., Sun, Y., and Blanke, M. (2017). Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks. J. Imaging, 3.","DOI":"10.3390\/jimaging3010006"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Biswas, U., Banerjee, A., Pal, S., Biswas, A., Sarkar, D., and Haldar, S. (2019). Advances in Computer, Communication and Control. Lecture Notes in Networks and Systems, Springer Singapore.","DOI":"10.1007\/978-981-13-3122-0"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.compag.2018.08.032","article-title":"Improving efficiency of organic farming by using a deep learning classification approach","volume":"153","author":"Knoll","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2017.02.002","article-title":"Weed segmentation using texture features extracted from wavelet sub-images","volume":"157","author":"Bakhshipour","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9567","DOI":"10.1007\/s11042-017-5337-y","article-title":"Image-based recognition framework for robotic weed control systems","volume":"77","author":"Kounalakis","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1109\/LRA.2018.2846289","article-title":"Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming","volume":"3","author":"Lottes","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lottes, P., and Stachniss, C. (2017, January 24\u201328). Semi-supervised online visual crop and weed classification in precision farming exploiting plant arrangement. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206403"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Milioto, A., Lottes, P., and Stachniss, C. (2018, January 21\u201325). Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460962"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.compind.2018.02.003","article-title":"Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture","volume":"98","author":"Bosilj","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/83.663500","article-title":"Antiextensive connected operators for image and sequence processing","volume":"7","author":"Salembier","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms, IEEE.","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.compag.2016.11.021","article-title":"Automatic crop detection under field conditions using the HSV colour space and morphological operations","volume":"133","author":"Hamuda","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","first-page":"1","article-title":"A Distributed Means Segmentation Algorithm Applied to Lobesia botrana Recognition","volume":"2017","author":"Pope","year":"2017","journal-title":"Complexity"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.compag.2017.01.020","article-title":"Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines","volume":"135","author":"Bromberg","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compag.2017.03.016","article-title":"Vision-based pest detection based on SVM classification method","volume":"137","author":"Ebrahimi","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","first-page":"7","article-title":"Automatic detection of nutritional deficiencies in coffee tree leaves through shape and texture descriptors","volume":"15","author":"Toledo","year":"2017","journal-title":"J. Digit. Inf. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compag.2017.04.008","article-title":"Symptom based automated detection of citrus diseases using color histogram and textural descriptors","volume":"138","author":"Ali","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_67","unstructured":"Zhang, C., Zhang, S., Yang, J., Shi, Y., and Chen, J. (2017). Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int. J. Agric. Biol. Eng., 10."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hlaing, C.S., and Zaw, S.M.M. (2017, January 24\u201327). Plant diseases recognition for smart farming using model-based statistical features. Proceedings of the 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), Nagoya, Japan.","DOI":"10.1109\/GCCE.2017.8229343"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.compag.2017.01.016","article-title":"Wheat leaf lesion color image segmentation with improved multichannel selection based on the Chan\u2013Vese model","volume":"135","author":"Hu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.eaef.2016.11.004","article-title":"Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features","volume":"10","author":"Patil","year":"2017","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Islam, M., Dinh, A., Wahid, K., and Bhowmik, P. (May, January 30). Detection of potato diseases using image segmentation and multiclass support vector machine. In Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.","DOI":"10.1109\/CCECE.2017.7946594"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.compag.2017.08.023","article-title":"A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing","volume":"142","author":"Ma","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Meyer, G.E., Hindman, T.W., and Laksmi, K. (1999, January 14). Machine Vision Detection Parameters for Plant Species Identification. Proceedings of the SPIE, Washington, DC, USA.","DOI":"10.1117\/12.336896"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compag.2014.07.004","article-title":"Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching","volume":"108","author":"Zhou","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Prakash, R.M., Saraswathy, G.P., Ramalakshmi, G., Mangaleswari, K.H., and Kaviya, T. (2017, January 17\u201318). Detection of leaf diseases and classification using digital image processing. Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India.","DOI":"10.1109\/ICIIECS.2017.8275915"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compag.2018.04.023","article-title":"Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection","volume":"150","author":"Sharif","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.compag.2017.01.014","article-title":"Leaf image based cucumber disease recognition using sparse representation classification","volume":"134","author":"Zhang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.ijleo.2017.11.190","article-title":"Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG","volume":"157","author":"Zhang","year":"2018","journal-title":"Optik"},{"key":"ref_80","first-page":"9","article-title":"Product Classification based on SVM and PHOG Descriptor","volume":"13","author":"Zhang","year":"2013","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2018.01.091","article-title":"Segmentation of images by color features: A survey","volume":"292","author":"Cervantes","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s11370-010-0075-2","article-title":"Applied machine vision of plants: A review with implications for field deployment in automated farming operations","volume":"3","author":"McCarthy","year":"2010","journal-title":"Intell. Serv. Robot."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1007\/s11119-016-9472-7","article-title":"An evaluation of the contribution of ultraviolet in fused multispectral images for invertebrate detection on green leaves","volume":"18","author":"Liu","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compind.2018.03.008","article-title":"Machine vision for orchard navigation","volume":"98","author":"Radcliffe","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0021-8634(72)80011-8","article-title":"An ultrasonic guidance system for driverless tractors","volume":"17","author":"Warner","year":"1972","journal-title":"J. Agric. Eng. Res."},{"key":"ref_86","first-page":"99","article-title":"Robotization of agricultural vehicles (part 1)-Component technologies and navigation systems","volume":"34","author":"Yukumoto","year":"2000","journal-title":"Jpn. Agric. Res. Q."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/S0168-1699(99)00055-1","article-title":"Automatic tractor guidance using carrier-phase differential GPS","volume":"25","author":"Bell","year":"2000","journal-title":"Comput. Electron. Agric."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.compag.2004.01.007","article-title":"A guidance directrix approach to vision-based vehicle guidance systems","volume":"43","author":"Han","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0168-1699(99)00052-6","article-title":"Guidance of agricultural vehicles\u2014A historical perspective","volume":"25","author":"Wilson","year":"2000","journal-title":"Comput. Electron. Agric."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0168-1699(96)00014-2","article-title":"Tracking of row structure in three crops using image analysis","volume":"15","author":"Marchant","year":"1996","journal-title":"Comput. Electron. Agric."},{"key":"ref_91","unstructured":"Zhang, Q., Reid, J.J.F., and Noguchi, N. (2019, December 09). Agricultural Vehicle Navigation Using Multiple Guidance Sensors. Available online: https:\/\/www.researchgate.net\/profile\/John_Reid10\/publication\/245235458_Agricultural_Vehicle_Navigation_Using_Multiple_Guidance_Sensors\/links\/543bce7c0cf2d6698be335dd\/Agricultural-Vehicle-Navigation-Using-Multiple-Guidance-Sensors.pdf."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.compag.2007.07.006","article-title":"A vision based row detection system for sugar beet","volume":"60","author":"Bakker","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Jiang, G.-Q., Zhao, C.-J., and Si, Y.-S. (2010, January 11\u201314). A machine vision based crop rows detection for agricultural robots. Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, China.","DOI":"10.1109\/ICWAPR.2010.5576422"},{"key":"ref_94","unstructured":"Jiang, G., and Zhao, C. (2010, January 22\u201324). A vision system based crop rows for agricultural mobile robot. Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.3390\/s110404086","article-title":"A New Approach to Visual-Based Sensory System for Navigation into Orange Groves","volume":"11","author":"Nebot","year":"2011","journal-title":"Sensors"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Sharifi, M., and Chen, X. (2015, January 17\u201319). A novel vision based row guidance approach for navigation of agricultural mobile robots in orchards. Proceedings of the 2015 6th International Conference on Automation, Robotics and Applications (ICARA), Queenstown, New Zealand.","DOI":"10.1109\/ICARA.2015.7081155"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.biosystemseng.2011.05.001","article-title":"Autonomous navigation using a robot platform in a sugar beet field","volume":"109","author":"Bakker","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Xue, J., and Xu, L. (2010, January 13\u201314). Autonomous Agricultural Robot and its Row Guidance. Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation, Changsha, China.","DOI":"10.1109\/ICMTMA.2010.251"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Scarfe, A.J., Flemmer, R.C., Bakker, H.H., and Flemmer, C.L. (2009, January 10\u201312). Development of an autonomous kiwifruit picking robot. Proceedings of the 2009 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand.","DOI":"10.1109\/ICARA.2000.4804023"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.biosystemseng.2014.02.010","article-title":"Image-based particle filtering for navigation in a semi-structured agricultural environment","volume":"121","author":"Hiremath","year":"2014","journal-title":"Biosyst. Eng."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.compag.2006.06.001","article-title":"Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation","volume":"53","author":"Subramanian","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.biosystemseng.2011.04.006","article-title":"Stereovision-based lateral offset measurement for vehicle navigation in cultivated stubble fields","volume":"109","author":"Wang","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compag.2012.02.009","article-title":"Variable field-of-view machine vision based row guidance of an agricultural robot","volume":"84","author":"Xue","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.mcm.2011.10.068","article-title":"Curve path detection of unstructured roads for the outdoor robot navigation","volume":"58","author":"Jiang","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.compag.2014.11.006","article-title":"Development of agricultural implement system based on machine vision and fuzzy control","volume":"112","author":"Meng","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1108\/01439910510582255","article-title":"A prototype of an orange picking robot: Past history, the new robot and experimental results","volume":"32","author":"Muscato","year":"2005","journal-title":"Ind. Robot-An Int. J."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ortiz, J.M., and Olivares, M. (2006, January 26\u201327). A Vision Based Navigation System for an Agricultural Field Robot. Proceedings of the 2006 IEEE 3rd Latin American Robotics, Symposium, Santiago, Chile.","DOI":"10.1109\/LARS.2006.334338"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.biosystemseng.2006.11.009","article-title":"Application Accuracy of a Machine Vision-controlled Robotic Micro-dosing System","volume":"96","author":"Lund","year":"2007","journal-title":"Biosyst. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"He, B., Liu, G., Ji, Y., Si, Y., and Gao, R. (2011). Auto Recognition of Navigation Path for Harvest Robot Based on Machine Vision. IFIP Advances in Information and Communication Technology, Springer.","DOI":"10.1007\/978-3-642-18333-1_19"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/5\/12\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:23Z","timestamp":1760190023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/5\/12\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,7]]},"references-count":109,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["jimaging5120089"],"URL":"https:\/\/doi.org\/10.3390\/jimaging5120089","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,7]]}}}