طراحی، ساخت و ارزیابی سامانه سمپاش خودکار به منظور تشخیص برخط علف‌هرز-گیاه در مزارع چغندرقند

Abbas I, Liu J, Faheem M, Noor RS, Shaikh SA, Solangi KA, Raza SM. Different real-time sensor technologies for the application of variable-rate spraying in agriculture. Sensors and Actuators A: Physical, 2020. 112265.

Abouzahir S, Sadik M, Sabir E. Bag-of-visual-words-augmented histogram of oriented gradients for efficient weed detection. Biosystems Engineering. 2021; 202: 179-194.

Ahmed F, Bari AH, Hossain E, Al-Mamun HA, Kwan P. Performance analysis of support vector machine and Bayesian classifier for crop and weed classification from digital images. World Applied Sciences Journal. 2011; 12(4): 432-440.

Anonymous. Yield and cultivated area of Iranian sugar beet in 2021. Iranian Sugar Factories Syndicate. 2021. http://www.isfs.ir (in Persian)

Anonymous. Food and Agriculture Organization (FAO) of the United Nations. FAOSTAT statistical database. 2019.

ASAE Standards S 217.12; Three-Point Free-Link Attachment for hitching implements to agricultural wheel tractors. American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2001.

Babu LG. Smart agriculture system with E– Carbage using Iot. International Journal of Modern Agriculture. 2021; 10(1): 928-931.

Bakhshipour Ziyaratgahi A, Jafari A, Emam Y, Nasiri SM, Kamgar S, Zare D. Use of modified huff in detection of sugar beet plant from grassland using machine vision. Journal of Agricultural Machinery. 2017; 7 (1): 73-85. (in Persian with English abstract)

Burgos- Artizzu XP, Ribeiro A, Guijarro M, Pajares G. Real-time image processing for crop-weed discrimination in maize fields. Computer and Electronic in Agriculture. 2011; 75(2): 337–346.

Cao X, Ning B, Yan P, Li X. Selecting key poses on manifold for pair-wise action recognition. Industrial Informatics, IEEE Transactions. 2012; 8(1): 168-177.

Chapron M, Requena-Esteso M, Boissard P, Assemat L. A method for recognizing vegetal species from multispectral images. 2^nd European Conference on Precision Agriculture Odense Congress Centre, Denmark. 1999.

Christensen S, Heisel T, Walter M. Patch spraying in cereals. Proc. of Second Int. Weed Control Congress, Copenhagen, Denmark. 1996; pp. 963–938.

Dammer KH. Real‐time variable‐rate herbicide application for weed control in carrots. Weed research; 2016. 56(3): 237-246.

Du JX, Wang XF, zhang GJ. Leaf shape based plant species Recognition. Applied Mathematics and Computation. 2007; 185: 883-893.

Garcia-Mateos G, Hernandez-Hernandez JL, Escarabajal- Henarejos D, Jaen-Terrones S, Molina-Martinez J M. Study and comparison of color models for automatic image analysis in irrigation management applications. Agricultural Water Management. 2015; 151, 158-66.

Gonzalez RC, Woods RE, Eddins SL. Digital image processing. Addison- Wesley Publishing Co, Reading, Mass., USA. 1992.

Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. Pearson Education India. 2004.

Guerrero JM, Guijarro M, Montalvo M, Romo J, Emmi L, Ribeiro A, Pajares G. Automatic expert system based on images for accuracy crop row detection in maize Fields. Expert Systems with Applications. 2013; 40: 656-664.

Guerrero JM, Pajares G, Montalvo M, Romo J, Guijarro M. Support vector machines for crop/weeds identification in maize fields. Expert Systems with Applications. 2012; 39: 11149-11155.

Guijarro M, Pajares G, Riomoros I, Herrera PJ, Burgose-Artizzu XP, Ribeiro A. Automatic segmentation of relevant textures in agricultural Images. Computers and Electronics in Agriculture. 2011: 75(1): 75-83.

Hamuda E, Mc Ginley B, Glavin M, Jones E. Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and Electronics in Agriculture. 2017; 133: 97–107.

Hasan AM, Sohel F, Diepeveen D, Laga H, Jones MG. A survey of deep learning techniques for weed detection from images. Computers and Electronics in Agriculture. 2021; 184, 106067.

HunterLab. Application note. Insight on Color. 2001; 13: 1-4.

ISO 730. Agricultural Wheeled Tractors—Rear-Mounted Three-Point Linkage Categories 1N, 1, 2N, 2, 3N, 3, 4N and 4. American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2009; 1.

Jafari A, Mohtasebi SS, Eghbali Jahromi H, Omid M. Presenting a suitable algorithm for separating weeds from sugar beet in real field conditions using image processing. International Journal of Agriculture and Biology. 2006; 8(5):602–605.

Jahada Akbar MR, Tabatabaei Nimavard R, Ebrahimian HD. Investigation of the critical period of sugar beet weed control in Kabutarabad Isfahan. Journal of Sugar Beet. 2004; 20(1): 73-92. (in Persian with English abstract)

Kakumanu P, Makrogiannis S, Bourbakis N. A survey of skin-color modeling and detection methods. Pattern Recognition. 2007; 40: 1106 – 1122.

 Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY. An efficient k-means clustering algorithm: Analysis and implementation. Pattern Analysis and Machine Intelligence, IEEE Transactions, 2002; 24(7): 881–892.

Lin WM, Hu YT. Image segmentation method based on YUV color space for tomato harvesting robot. Trans. Chin. Society of Agricultural Engineering. 2012; 43(12): 176–180.

Liu B, Bruch R. Weed detection for selective spraying: A review. Current Robotics Reports. 2020; 1(1): 19-26.

Luzuriaga DA, Balaban MO. Colour machine vision system: an alternative for color measurement. In Proceedings of the world congress of computers in agriculture and natural resources, Iguacu Falls, Brazil, 2002; 13–15 March. pp. 93–100.

Meyer GE, Hindman TW, Lakshmi K. Machine vision detection parameters for plant species identification. In: Meyer, G.E., DeShazer, J.A. (Eds.), Precision Agriculture and Biological Quality, Proceedings of SPIE. 1998; 3543: 327–335.

Mohammad Zamani D, Minaei S, Alimardani R, Almasi M, Norouz poordeilami H, Rostami A. Design and fabrication of a map- based VRA system for a tractor- mounted boom-type field sprayer. Iranian Journal of Biosystems Engineering. 2013; 43(2): 111-123. (in Persian with English abstract)

Montazeri EZ, Jodaii A, Zomorodi S, Hanaeh M, Habibi R. Investigating the effects of nitrogen and animal manure (poultry manure) and their mutual effects on the quantity and quality of tomatoes. Soil and Water Research Institute. Plan No. 102-15-19-82-059. 2013. (in Persian)

Nadafzadeh M, Mehdizadeh SA. Design and fabrication of an intelligent control system for determination of watering time for turfgrass plant using computer vision system and artificial neural network. Precision Agriculture. 2019; 20(5): 857-879.

Ohta Y, Kanade T, Sakai T. Color information for region segmentation. Computer. Graphics Image Process, 1980; 13(3): 222–241.

Ohta Y, Kanade T, Sakai T. Color information for region segmentation. Computer Graphics and Image Processing. 1980; 13: 222–241.

Oluleye B, Leisa A, Leng L, Dean D. A Genetic Algorithm-Based Feature Selection. International Journal of Electronics Communication and Computer Engineering. 2014; 5(4) 2249–071.

Perez AJ, Lopez F, Benlloch JV, Christensen S. Color and shape analysis techniques for weed detection in cereal fields. Computer and Electronic in Agriculture. 2000; 25(3): 197–212.

Philipp I, Rath T. Improving plant discrimination in image processing by use of different colour space transformations. Computers and Electronics in Agriculture. 2002; 35: 1–15.

Rani SV, Kumar PS, Priyadharsini R, Srividya SJ, Harshana S. Automated weed detection system in smart farming for developing sustainable agriculture. International Journal of Environmental Science and Technology. 2021; 1-12.‏

Sabzi S, Abbaspour-Gilandeh Y, Arribas JI. an automatic visible-range video weed detection. Segmentation and classification prototype in potato field, Heliyon. 2020; 6(5): 03685.

Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G. A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. Computers in Industry. 2018; 98: 80–89.

Sester M, Delanoy M, Colbach N, Darmency H. Crop and density effect on weed beet growth and reproduction. Weed Research. 2004; 44: 50–59.

Sharma A. Understanding Color Management. Thomson Delmar. New York. 2004.

Skrubej U, Rozman C, Stajnko D. Assessment of germination rate of the tomato seeds using image processing and machine learning.  European Journal of Horticultural Science. 2015; 80(2): 68–75.

Subr A, Parafiniuk S, Milanowski M, Krawczuk A, Kachel M. Study of deposited spray quality of spraying agents with different physical properties. Plant archives. 2020; 20(2): 6109-6114.

Tang J, Wang D, Zhang Z, He L, Xin J, Xu Y. Weed identification based on K-means feature learning combined with convolutional neural network. Computers and Electronics in Agriculture. 2017; 135: 63–70.

Tewari VK, Pareek CM, Lal G, Dhruw LK, Singh N. Image processing based real-time variable-rate chemical spraying system for disease control in paddy crop. Artificial Intelligence in Agriculture. 2020; 4: 21-30.

Truppel I, Herold B, Geyer M. Einfluß der beleuchtung bei der qualita¨tsbeurteilung von fru¨chten mittels Computerbildanalyse (Influence of light on quality assessment of fruits by use of image processing). Gartenbauwissenschaft. 1998; 63, 54–63.

Uz Zamana Q, Esaua TJ, Schumann AW, Percivalc DC, Chang YK, Read SM, Farooquea AA. Development of prototype automated variable rate sprayer for real-time spot-application of agrochemicals in wild blueberry fields. Computers and Electronics in Agriculture. 2011; 76: 175–182.

Watchareeruetai U, Takeuchi Y, Matsumoto T, Kudo H, Ohnishi N. Computer vision based methods for detecting weeds in lawns. Machine Vision and Applications. 2006; 17(5): 287-296.

Wiles LJ. Software to quantify and map vegetative cover in fallow fields for weed management decisions. Computers and Electronics in Agriculture. 2011; 78(1): 106–11.

Zoschke A, Quadranti M. Integrated weed management: Quo vadis. Weed Biology. 2002; 2: 1-10.