فناوری ماشین‌بینایی در کشاورزی دقیق: مروری جامع بر اصول و کاربردها

نویسندگان

1 گروه فنی کشاورزی، پردیس ابوریحان، دانشگاه تهران

2 دانشیار گروه مهندسی مکانیک بیوسیستم، پردیس ابوریحان، دانشگاه تهران

3 استادیار گروه مهندسی مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

چکیده

رشد جمعیت جوامع و ارتقای سطح زندگی، لزوم بهره­گیری از روش­های جدید به­منظور تهیه مواد غذایی سالم و با کیفیت را پدید آورده است. با توجه به توسعه چشمگیر روش‌های پردازش تصویر و ماشین‌بینائی، تحقیقات بسیاری به­منظور ارائه یک راه حل مبتنی بر پردازش تصویر در مسائل مختلف و رو به رشد کشاورزی دقیق ارائه شده است. در حال حاضر، محققین به­طور گسترده از پردازش تصویر برای هوشمندسازی بخشی از مراحل مختلف زراعت و باغبانی، از جمله کاشت، کنترل علف‌های هرز، آبیاری، سمپاشی، کودپاشی، بررسی روند رشد گیاه و برداشت محصول استفاده کرده‌اند. این روش‌های هوشمندسازی در بخش کشاورزی علاوه بر کاهش هزینه­های تولید و نهاده‌های کشاورزی، اثرات چشم­گیری نیز در حفظ منابع آب و محیط زیست ایفا می­کنند. در این بررسی مروری، روش‌های مبتنی بر پردازش تصویر که تاکنون برای هوشمندسازی کنترل علف‌های هرز و مدیریت موضعی سمپاشی و آبیاری معرفی شده‌اند، مورد بررسی قرار گرفته‌اند. همچنین تلاش شده است تا عملکرد هر روش به­همراه مزایا و معایب آن بررسی شود. در نهایت، چشم‌انداز آینده کاربردهای پردازش تصویر در حوزه‌های مورد بررسی کشاورزی دقیق ترسیم شده است.

کلیدواژه‌ها


عنوان مقاله [English]

The machine vision technology in precision agriculture: A comprehensive review on principles and applications

نویسندگان [English]

  • Marzieh Ghorbani 1
  • Mohamad Aboonajmi 2
  • Keyvan Asefpour Vakilian 3
چکیده [English]

Population growth and the improvement of the living level have resulted in the use of new methods to produce healthy and quality food. Due to the remarkable development of image processing and machine vision methods, a lot of research has been presented to provide an effective solution based on image processing in various fields of precision agriculture. Researchers have currently widely used image processing in smart farming and gardening, including planting, weed control, irrigation, spraying, fertilization, plant growth monitoring, and crop harvesting. In addition to reducing production costs and agricultural inputs, these methods also have significant effects on the conservation of water resources and protecting the environment. In this review study, image processing-based methods that have been introduced so far for smart weed control, local spraying, and irrigation management are investigated. It is also attempted to evaluate the performance of each method with its advantages and disadvantages. Finally, the future perspectives of image processing applications in the fields of precision agriculture are expressed.

کلیدواژه‌ها [English]

  • machine vision
  • Precision agriculture
  • Feature extraction
  • Local management
  • Weed control
  1. [1] Das J., Cross G., Qu C., Makineni A., Tokekar P., Mulgaonkar Y., and Kumar V., Devices, systems, and methods for automated monitoring enabling precision agriculture, In 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp. 462-469., 2015. [2] Plant R. E., “Site-specific management: the application of information technology to crop production”, Computers and Electronics in Agriculture, Vol. 30, pp. 9-29, 2001. [3] Mulla D. J., “Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps”, Biosystems Engineering, Vol. 114, No. 4, pp. 358-371, 2013. [4] Earl R., Wheeler P. N., Blackmore B. S., and Godwin R. J., “Precision farming: the management of variability”, Landwards, Vol. 51, pp. 18-23, 1996. [5] Sabatier P., Poulenard J., Fangeta B., Reyss J. L., Develle A. L., Wilhelm B., Ployon E., Pignol C., Naffrechoux E., Dorioz J. M., Montuelle B., and Arnaud F., “Long-term relationships among pesticide applications, mobility, and soil erosion in a vineyard watershed”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 111, pp. 15647-15652, 2014. [6] Stehle S., and Schulz R., “Agricultural insecticides threaten surface waters at the global scale”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 112, pp. 5750-5755, 2015. [7] Abdollahi M., Ranjbar A., Shadnia S., Nikfar S., and Rezaie A., “Pesticides and oxidative stress: a review”, Medical Science Monitor, Vol. 10, pp. RA141-RA147, 2004. [8] Burns C. J., McIntosh L. J., Mink P. J., Jurek A. M., and Li A. A., “Pesticide exposure and neurodevelopmental outcomes: review of the epidemiologic and animal studies”, Journal of Toxicology and Environmental Health e Part B Critical Reviews, Vol. 16, pp. 127-283, 2013. [9] Rauh V. A., Perera F. P., Horton M. K., Whyatt R. M., Bansal R., Hao X., Liu J., Boyd Barr D., Slotkin T. A., and Peterson B. S., “Brain anomalies in children exposed prenatally to a common organophosphate pesticide”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 109, pp. 7871-7876, 2012. [10] National Research Council, Precision Agriculture in the 21st Century-Geospatial and Information Technologies, Crop Management, National Academy Press, Washington, D.C, 168 pages, 1997. [11] Atzberger C., “Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs”, Remote sensing, Vol. 5, pp. 949-981, 2013. [12] Steele-Dunne S. C., McNairn H., Monsivais-Huertero A., Judge J., Liu P. W., and Papathanassiou K., “Radar remote sensing of agricultural canopies: A review”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, pp. 2249-2273, 2017. [13] Faridi H., and Abonajmi M., “Application of machine vision in agricultural products”, Proceedings of the 4th Iranian International NDT Conference, Olympic Hotel, Tehran, Iran, 2017. [14] Bechar A., and Vigneault C., “Agricultural robots for field operations: Concepts andcomponents”, Biosystems Engineering, Vol. 149, pp. 94-111, 2016. [15] Bac C. W., Hemming J., Van Tuijl B. A. J., Barth R., Wais E., and Van Henten E. J., “Performance evaluation of a harvesting robot for sweet pepper”, Journal of Field Robotics, Vol. 34, pp. 1123-1139, 2017. [16] Menges R. M., Nixon P. R., and Richardson A. J., “Light reflectance and remote sensing of weeds in agronomic and horticultural crops”, Weed Science, Vol. 33, pp. 569-581, 1985. [17] Richardson A. J., Menges R. M., and Nixon P. R., “Distinguishing weed from crop plants using video remote sensing”, Photogrammetric Engineering and Remote Sensing, Vol. 51, pp. 1785-1790, 1985. [18] [18] Lopez-Granados F., Jurado-Exposito M., Atenciano S., García-Ferrer A., Sánchez de la Orden M., and García-Torres L., “Spatial variability of agricultural soils in southern Spain”, Plant and Soil, Vol. 246, pp. 97-105, 2002. [19] Xavier P., Artizzu B., Ribeiro A., Tellaeche A., Pajares G., and Fernandez-Quintanilla C., “Analysis of natural images processing for the extraction of agricultural elements”, Image and Vision Computing, Vol. 28, pp. 138-149, 2010. [20] Bacco M., Berton A., Ferro E., Gennaro C., Gotta A., Matteoli S., and Zanella A., “Smart farming: Opportunities, challenges and technology enablers”, In 2018 IoT Vertical and Topical Summit on Agriculture-Tuscany (IOT Tuscany) IEEE, pp. 1-6, 2018. [21] Abouzar P., Michelson D. G., and Hamdi M., “RSSI-based distributed self-localization for wireless sensor networks used in precision agriculture”, IEEE Transactions on Wireless Communications, Vol. 15, pp. 6638-6650, 2016. [22] Huang Y., Lee M. A., Thomson S. J., and Reddy K. N., “Ground-based hyperspectral remote sensing for weed management in crop production”, International Journal of Agricultural and Biological Engineering, Vol. 9, pp. 98-109, 2016. [23] Rokhmana C. A., “The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia”, Procedia Environmental Sciences, Vol. 24, pp. 245-253, 2015. [24] Deere J., Ess D., and Morgan, M., The Precision-Farming Guide for Agriculturists, Deere & Company, Technology & Engineering, Moline, IL, USA, 2003. [25] Asefpour Vakilian K., and Massah J., “A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops”, Computers and electronics in agriculture, Vol. 139, pp. 153-163, 2017. [26] Zarco-Tejada P. J., González-Dugo M. V., and Fereres E., “Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture”, Remote Sensing of Environment, Vol. 179, pp. 89-103, 2016. [27] فیاضی، ح، دهقانی، ح، حسینی، س م، «مروری بر استفاده از محاسبات تکاملی در تجزیه طیفی تصاویر ابرطیفی»، نشریه علمی محاسبات نرم، دوره 1، شماره 2، صفحه 59-46، 1391. [28] Guijarro M., Riomoros I., Pajares G., and Zitinski P., “Discrete wavelets transform for improving greenness image segmentation in agricultural images”, Computers and Electronics in Agriculture, Vol. 118, pp. 396-407, 2015. [29] Hamuda E., Glavin M., and Jones E., “A survey of image processing techniques for plant extraction and segmentation in the field”, Computers and Electronics in Agriculture, Vol. 125, pp. 184-199, 2016. [30] Pérez A., Lopez F., Benlloch J., and Christensen S., “Colour and shape analysis techniques for weed detection in celeral fields”, Computers and Electronics in Agriculture, Vol. 25, pp. 197-212, 2000. [31] Aitkenhead M., Dalgetty I., Mullins C., McDonald A., and Strachan N., “Weed and crop discrimination using image analysis and artificial intelligence methods”, Computers and Electronics in Agriculture, Vol. 39, pp. 157-171, 2003. [32] Ribeiro A., Fernandez-Quintanilla C., Barroso J., and García-Alegre M. C., “Development of an image analysis system for estimation of weed”, Proceedings of the 5th European Conference on Precision Agriculture (5ECPA), 169-174, 2005. [33] Van Evert F. K., Van Der Heijden G. W., Lotz L. A. P., Polder G., Lamaker A., De Jong A., Kuyper M. C., Groendijk E. J., Neeteson J. J., and Van Der Zalm T., “A mobile field robot with vision-based detection of volunteer potato plants in a corn crop”, Weed Technology, Vol. 20, pp. 853-861, 2006. [34] Meyer G., and Neto J., “Verification of color vegetation indices for automated crop imaging applications”, Computers and Electronics in Agriculture, Vol. 63, pp. 282-293, 2008. [35] Hemming J., and Rath T., “Computer-vision based weed identification under field conditions using controlled lighting”, Journal of Agricultural Engineering Research, Vol. 78, pp. 233-243, 2001. [36] Blasco J., Aleixos N., Roger J., Rabatel G., and Molto E., “Robotic weed control using machine vision”, Biosystems Engineering, Vol. 83, pp. 149-157, 2002. [37] سیدیزدی، س ج، حسن‌پور، ح، «ابر تفکیک‌پذیری: مروری بر روش‌های موجود»، نشریه علمی محاسبات نرم، دوره 2، شماره 2، صفحه 51-36، 1392. [38] Mart J., Freixenet J., Batlle J., and Casals A., “A new approach to outdoor scene description based on learning and top-down segmentation”, Image and Vision Computing, Vol. 19, pp. 1041-1055, 2001. [39] Bosch A., Muoz X., and Freixenet J., “Segmentation and description of natural outdoor scenes”, Image and Vision Computing, Vol. 25, pp. 727-740, 2007. [40] Gerhards R., and Christensen S., “Real-time weed detection, decision making and patch spraying in maize sugarbeet winter wheat and winter barley”, Weed Research, Vol. 43, pp. 385-392, 2003. [41] García-Mateos G., Hernández-Hernández J. L., Escarabajal-Henarejos D., Jaén-Terrones S., and Molina-Martínez J. M., “Study and comparison of color models for automatic image analysis in irrigation management applications”, Agricultural water management, Vol. 151, pp. 158-166, 2015. [42] Zheng Y., Zhu Q., Huang M., Guo Y., and Qin J., “Maize and weed classification using color indices with support vector data description in outdoor fields”, Computers and electronics in agriculture, Vol. 141, pp. 215-222, 2017. [43] Woebbecke D. M., Meyer G. E., Von Bargen K., and Mortensen D. A., “Plant species identification, size, and enumeration using machine vision techniques on near- binary images”, In: DeShazer, J.A., Meyer, G.E. (Eds.), International Society for Optics and Photonics, pp. 208-219, 1993. [44] Guerrero J. M., Pajares G., Montalvo M., Romeo J., and Guijarro M., “Support vector machines for crop/weeds identification in maize fields”, Expert Systems with Applications, Vol. 39, pp. 11149-11155, 2012. [45] Saha D., Hanson A., and Shin S. Y., “Development of Enhanced Weed Detection System with Adaptive Thresholding and Support Vector Machine”, In: The International Conference. ACM Press, New York, New York, USA, 85-88, 2016. [46] Woebbecke D. M., Meyer G. E., Bargen K. Von., and Mortensen D. A., “Color indices for weed identification under various soil, residue, and lighting conditions”, Trans. ASAE. Vol. 38, pp. 259-269, 1995. [47] Chris Gliever D. C. S., Crop verses Weed Recognition with Artificial Neural Networks, Sacramento, American Society of Agricultural and Biological Engineers, St. Joseph, MI, p. 1, CA July 29-August 1, 2001. [48] Mathanker S. K., Weckler P. R., Taylor R. K., and Fan G., “Adaboost and Support Vector Machine Classifiers for Automatic Weed Control: Canola and Wheat, Pittsburgh, Pennsylvania”, American Society of Agricultural and Biological Engineers, St. Joseph, MI, p. 1, 2010. [49] Meyer G. E., Hindman T. W., and Laksmi K., “Machine vision detection parameters for plant species identification”, In: Meyer, G.E., DeShazer, J.A. (Eds.), International Society for Optics and Photonics, pp. 327-335, 1999. [50] Kataoka T., Kaneko T., Okamoto H., and Hata S., “Crop growth estimation system using machine vision”, In: Proceedings IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), IEEE, b1079-b1083, 2003. [51] Guerrero J. M., Pajares G., Montalvo M., Romeo J., and Guijarro M., “Support vector machines for crop/weeds identification in maize fields”, Expert Systems with Applications, Vol. 39, pp. 11149-11155, 2012. [52] Meyer G. E., Camargo Neto J., Jones D. D., and Hindman T. W., “Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images”, Comput. Electron. Agric, Vol. 42, pp. 161-180, 2004. [53] Meyer G. E., and Neto J. C., “Verification of color vegetation indices for automated crop imaging applications”, Comput. Electron. Agric, Vol. 63, pp. 282-293, 2008. [54] Hunt E.R., Cavigelli M., Daughtry C. S. T., Mcmurtrey J. E., and Walthall C. L., “Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status”, Precis. Agric, Vol. 6, pp. 359-378, 2005. [55] Hague T., Tillett N. D., and Wheeler H., “Automated crop and weed monitoring in widely spaced cereals”, Precis. Agric, Vol. 7, pp. 21-32, 2006. [56] Burgos-Artizzu X. P., Ribeiro A., Guijarro M., and Pajares G., “Real-time image processing for crop/weed discrimination in maize fields”, Comput. Electron. Agric, Vol. 75, pp. 337-346, 2011. [57] Ishak A. J., Hussain A., and Mustafa M. M., “Weed image classification using Gabor wavelet and gradient field distribution”, Comput. Electron. Agric, Vol. 66, pp. 53-61, 2009. [58] Wu X., Xu W., Song Y., and Cai M., “A detection method of weed in wheat field on machine vision”, Procedia Eng, Vol. 15, pp. 1998-2003, 2011. [59] Jeon H.Y., Tian L.F., and Zhu H., “Robust crop and weed segmentation under uncontrolled outdoor illumination”, Sensors, Vol. 11, pp. 6270-6283, 2011. [60] Bakhshipour A., and Jafari A., “Evaluation of support vector machine and artificial neural networks in weed detection using shape features”, Comput. Electron. Agric, Vol. 145, pp. 153-160, 2018. [61] Guijarro M., Pajares G., Riomoros I., Herrera P. J., Burgos-Artizzu X. P., and Ribeiro A., “Automatic segmentation of relevant textures in agricultural images”, Comput. Electron. Agric, Vol. 75, pp. 75-83, 2011. [62] Prema P., and Murugan D., “A novel angular texture pattern (ATP) extraction method for crop and weed discrimination using curvelet transformation”, ELCVIA Electron. Lett. Comput. Vis. Image Anal, Vol. 15, pp. 27-59, 2016. [63] Haug S., Michaels A., and Biber P., and Ostermann J., “Plant classification system for crop/weed discrimination without segmentation”, IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1142-1149, 2014. [64] Lottes P., Hoeferlin M., Sander S., Muter M., Schulze P., and Stachniss L. C., “An effective classification system for separating sugar beets and weeds for precision farming applications”, IEEE International Conference on Robotics and Automation (ICRA), pp. 5157-5163, 2016. [65] Lottes P., Horferlin M., Sander S., and Stachniss C., “Effective vision-based classification for separating sugar beets and weeds for precision farming”, J. F. Robot, Vol. 34, pp. 1160-1178, 2016. [66] Potena C., Nardi D., and Pretto A., “Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture”, In: Advances in Robot Design and Intelligent Control, Springer International Publishing, Cham, pp. 105-121, 2017. [67] Golzarian M. R., and Frick R. A., “Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis”, Plant Methods, Vol. 7, pp. 1-11, 2011. [68] Kazmi W., Garcia-Ruiz F., Nielsen J., Rasmussen J., and Andersen H. J., “Exploiting affine invariant regions and leaf edge shapes for weed detection”, Comput. Electron. Agric, Vol. 118, pp. 290-299, 2015. [69] Barrero O., and Perdomo S. A., “RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields”, Precision agriculture, Vol. 19, pp. 809-822, 2018. [70] Piron A., Leemans V., Kleynen O., Lebeau F., and Destain M. F., “Selection of the most efficient wavelength bands for discriminating weeds from crop”, Computers and Electronics in Agriculture, Vol. 62, pp. 141-148, 2008. [71] Souza M. F. de., Amaral L. R. do., Oliveira S. R. de. M., Coutinho M. A. N., and Ferreira Netto C., “Spectral differentiation of sugarcane from weeds”, Biosystems Engineering, Vol. 190, pp. 41-46, 2020. [72] Lee W. S., Slaughter D., and Ken Giles D., “Robotic weed control system for tomatoes using machine vision and precision chemical application”, Precision Agriculture, Vol. 1, pp. 95-113, 1999. [73] Cheng B., and Matson E.T., “A feature-Based machine learning agent for automatic rice and weed discrimination”, Springer, Cham, pp. 517-527, 2015. [74] Ahmed F., Al-Mamun H. A., Bari A. S. M. H., Hossain E., and Kwan P., “Classification of crops and weeds from digital images: a support vector machine approach”, Crop Prot, Vol. 40, pp. 98-104, 2012. [75] Hao P., Wang L., and Niu Z., “Comparison of hybrid classifiers for crop classification using normalized difference vegetation index time series: a case study for major crops in North Xinjiang”, China. PLoS One, 10, e0137748-24, 2015. [76] Li N., Grift T. E., Yuan T., Zhang C., Momin M. A., and Li W., “Image processing for crop/weed discrimination in fields with high weed pressure, ASABE International Meeting”, American Society of Agricultural and Biological Engineers, pp. 1-11, 2016. [77] Sabzi S., Abbaspour-Gilandeh Y., and Garcia-Mateos G., “A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms”, Comput. Ind, Vol. 98, pp. 80-89, 2018. [78] Shirzadifar A. M., Loghavi M., and Raoufat M. H., “Development and Evaluation of a Real Time Site-Specific Inter-Row Weed Management System”, Iran Agricultural Research, Vol. 32, 2013. [79] Asefpour Vakilian K., and Massah J., “Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks”, Archives of phytopathology and plant protection, Vol. 46, pp. 1262-1269, 2013. [80] Slaughter D. C., Giles D. K., and Downey D., “Autonomous robotic weed control systems: A review”, Computers and electronics in agriculture, Vol. 61, pp. 63-78, 2008. [81] Weis M., and Sökefeld M., “Detection and identification of weeds, In Precision crop protection-the challenge and use of heterogeneity”, Springer, Dordrecht, pp. 119-134, 2010. [82] Jeon H. Y., Tian L. F., and Zhu H., “Robust crop and weed segmentation under uncontrolled outdoor illumination”, Sensors, Vol. 11, pp. 6270-6283, 2011. [83] Das S., “Systematics and taxonomic delimitation of vegetable, grain and weed amaranths: a morphological and biochemical approach”, Genetic resources and crop evolution, Vol. 59, pp. 289-303, 2012. [84] Herrera P., Dorado J., and Ribeiro A., “A novel approach for weed type classification based on shape descriptors and a fuzzy decision-making method”, Sensors, Vol. 14, pp. 15304-15324, 2014. [85] Mursalin M., and Mesbah-Ul-Awal M., “Towards Classification of Weeds through Digital Image”, Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 1-4, 2014. [86] Rumpf T., Roemer C., Weis M., Soekefeld M., Gerhards R., and Pluemer L., “Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine”, Comput. Electron. Agric, Vol. 80, pp. 89-96, 2012. [87] Pereira L. A. M., Nakamura R. Y. M., de Souza G. F. S., Martins D., and Papa J. P., “Aquatic weed automatic classification using machine learning techniques”, Comput. Electron. Agric, Vol. 87, pp. 56-63, 2012. [88] Tannouche A., Sbai K., Rahmoune M., Zoubir A., Agounoune R., Saadani R., and Rahmani A., “A Fast and Efficient Shape Descriptor for an Advanced Weed Type Classification Approach”, International Journal of Electrical & Computer Engineering, Vol. 6, pp. 2088-8708, 2016. [89] Chen Z., Wang L., Wu W., Jiang Z., and Li H., “Monitoring plastic-mulched farmland by Landsat-8 OLI imagery using spectral and textural features”, Remote Sensing, Vol. 8, p. 353, 2016. [90] Lin F., Zhang D., Huang Y., Wang X., and Chen X., “Detection of corn and weed species by the combination of spectral”, shape and textural features, Sustainability, Vol. 9, p. 1335, 2017. [91] Vakilian K. A., and Massah J., “An apple grading system according to European fruit quality standards using Gabor filter and artificial neural networks, Scientific Study & Research”, Chemistry & Chemical Engineering, Biotechnology, Food Industry, Vol. 17, 75, 2016. [92] Minaee S., Abdolrashidi A., and Wang Y., “Iris recognition using scattering transform and textural features”, IEEE signal processing and signal processing education workshop (SP/SPE), IEEE, August, pp. 37-42, 2015. [93] Sujaritha M., Annadurai S., Satheeshkumar J., Sharan S. K., and Mahesh L., “Weed detecting robot in sugarcane fields using fuzzy real time classifier”, Computers and electronics in agriculture, Vol. 134, pp. 160-171, 2017. [94] Bossu J., G´ee C., and Truchetet F., “Development of a machine vision system for a real time precision sprayer”, Electronic Letters on Computer Vision and Image Analysis, Vol. 7, pp. 54-66, 2008. [95] Utstumo T., Urdal F., Brevik A., Dørum J., Netland J., Overskeid Ø., and Gravdahl J. T., “Robotic in-row weed control in vegetables”, Computers and electronics in agriculture, Vol. 154, pp. 36-45, 2018. [96] Campos Y., Sossa H., and Pajares G., “Comparative analysis of texture descriptors in maize fields with plants, soil and object discrimination”, Precision agriculture, Vol. 18, pp. 717-735, 2017. [97] Ahmad J., Muhammad K., Ahmad I., Ahmad W., Smith M. L., Smith L. N., and Mehmood I., “Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems”, Computers in Industry, Vol. 98, pp. 23-33, 2018. [98] Geé C., Bossu J., Jones G., and Truchetet F., “Crop weed discrimination in perspective agronomic images”, Computers and Electronics in Agriculture, Vol. 60, pp. 49-59, 2008. [99] Tellaeche A., BurgosArtizzu X. P., Pajares G., Ribeiro A., and Fernández-Quintanillad C., “A new vision-based approach to differential spraying in precision agriculture”, Computers and Electronics in Agriculture, Vol. 60, pp. 144-155, 2008. [100] Olsen H., “Determination of row position in small-grain crops by analysis of video images”, Computers and Electronics in Agriculture, Vol. 12, pp. 147-162, 1995. [101] Sogaard H., and Olsen H., “Determination of crop rows by image analysis without segmentation”, Computers and Electronics in Agriculture, Vol. 38, pp. 141-158, 2003. [102] Sapkota B., Singh V., Cope D., Valasek J., and Bagavathiannan, M., “Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery”, AgriEngineering, Vol. 2, pp. 350-366; 2020. [103] Chen Y., Zhu H., Ozkan H., Derksen R., and Krause C., “Spray drift and off-target loss reduction with a precision air-assisted sprayer”, Transactions of the American Society of Agricultural and Biological Engineers, Vol. 56, pp. 1273-1281, 2013. [104] Zhu H., Derksen R., Guler H., Krause C., and Ozkan H., “Foliar deposition and off-target loss with different spray techniques in nursery applications”, Transactions of the American Society of Agricultural and Biological Engineers, Vol. 49, pp. 325-334, 2006. [105] Zohaib M., Kim H., and Choi M., “Detecting global irrigated areas by using satellite and reanalysis products”, Science of The Total Environment, Vol. 677, pp. 679-691, 2019. [106] Mendes W. R., Araújo F. M. U., Dutta R., and Heeren D. M., “Fuzzy control system for variable rate irrigation using remote sensing”, Expert Systems with Applications, Vol. 124, pp. 13-24, 2019. [107] Ma S., Zhou Y., Gowda P. H., Dong J., Zhang G., Kakani V. G., and Jiang W., “Application of the water-related spectral reflectance indices: A review”, Ecological indicators, Vol. 98, pp. 68-79, 2019. [108] Chakraborty M., Khot L. R., and Peters R. T., “Assessing suitability of modified center pivot irrigation systems in corn production using low altitude aerial imaging techniques”, Information Processing in Agriculture, 2019. [109] Chen A., Orlov-Levin V., and Meron M., “Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management”, Agricultural water management, Vol. 216, pp. 196-205, 2019. [110] Maghsoudi H., Minaei S., Ghobadian B., and Masoudi H., “Ultrasonic sensing of pistachio canopy for low-volume precision spraying”, Computers and Electronics in Agriculture, Vol. 112, pp. 149-160, 2015. [111] Hoçevar M., Sirok B., Jejcic V., Godesa T., Lesnik M., and Stajnko D., “Design and testing of an automated system for targeted spraying in orchards”, Journal of Plant Diseases and Protection, Vol. 117, pp. 71-79, 2010. [112] Lee K. H., and Ehsani R., “A Laser Scanner Based Measurement System for Quantification of Citrus Tree Geometric Characteristics”, Applied Engineering in Agriculture, Vol. 25, pp. 777-788, 2009. [113] آسائی، ه، جعفری، ع، لغوی، م، «ساخت و ارزیابی سمپاش هدفدار باغی با استفاده از فناوری ماشین بینایی»، مجله ماشینهای کشاورزی، دوره 6، شماره 2، صفحه 375-362، 1395. [114] Oberti R., Marchi M., Tirelli P., Calcante A., Iriti M., Tona E., Ho_cevar M., Baur J., Pfaff J., Schutz C., and Ulbrich H., “Spraying of grapevines for disease control using a modular agricultural robot”, Biosystems engineering, Vol. 146, pp. 203-215, 2016. [115] نداف‌زاده، م، آبدانان مهدی‌زاده، س، آسودار، م، صالحی سلمی، م، «طراحی و توسعه سامانه کنترل هوشمند تعیین آب مورد نیاز گیاهان گلخانه‌ای با کمک بینایی ماشین (مورد مطالعه: گیاه حُسنِ‌یوسف)»، مجله مهندسی بیوسیستم ایران، دوره 48، شماره 2، صفحه 297-285، 1396. [116] نداف‌زاده، م، آبدانان مهدی‌زاده، س، صالحی سلمی، م، «پیش‌بینی و کنترل محتوای رطوبت گیاه چمن توسط یک سامانه هوشمند با بکارگیری پردازش تصویر و الگوریتم رگرسیون بردار پشتیبان»، مجله ماشین بینایی و پردازش تصویر، دوره ۵، شماره ۲، صفحه 102-85، 1397. [117] Hussain M., Naqvi S. H. A., Khan S. H., and Farhan M., "An Intelligent Autonomous Robotic System for Precision Farming", 3rd International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore, 2020, pp. 133-139. [118] Hejazipoor H., Massah J., Soryani M., Asefpour Vakilian K., and Chegini G., “An intelligent spraying robot based on plant bulk volume”, Computers and Electronics in Agriculture, Vol. 180, 105859, 2021. [119] سلطانی محمدی، س، لک، م، محمدی، س، کربلا، م ا، «تخمین ارتفاع سطح ایستابی در روزهای مختلف سال با استفاده از شبکه عصبی مصنوعی شعاعی؛ مطالعه موردی: دشت بهبهان»، نشریه علمی محاسبات نرم، دوره 3، شماره 1، صفحه 93-82، 1393.