Mohammadikia, R., sadraddini, A., nazemi, A., delearhasannia, R., onnabi milani, A. (2022). Determination of Crop Coefficient of Sugar Beet by Proximal Sensing Method Using Digital Images. , 36(2), 203-216. doi: 10.22092/jwra.2022.358250.920
Reza Mohammadikia; ali ashraf sadraddini; amir hossein nazemi; Reza delearhasannia; ajdar onnabi milani. "Determination of Crop Coefficient of Sugar Beet by Proximal Sensing Method Using Digital Images". , 36, 2, 2022, 203-216. doi: 10.22092/jwra.2022.358250.920
Mohammadikia, R., sadraddini, A., nazemi, A., delearhasannia, R., onnabi milani, A. (2022). 'Determination of Crop Coefficient of Sugar Beet by Proximal Sensing Method Using Digital Images', , 36(2), pp. 203-216. doi: 10.22092/jwra.2022.358250.920
Mohammadikia, R., sadraddini, A., nazemi, A., delearhasannia, R., onnabi milani, A. Determination of Crop Coefficient of Sugar Beet by Proximal Sensing Method Using Digital Images. , 2022; 36(2): 203-216. doi: 10.22092/jwra.2022.358250.920

Determination of Crop Coefficient of Sugar Beet by Proximal Sensing Method Using Digital Images

پژوهش آب در کشاورزی
Volume 36, Issue 2, September 2022, Pages 203-216    PDF (1.29 M)
Document Type: Research Paper
DOI: 10.22092/jwra.2022.358250.920
Authors
Reza Mohammadikia1; ali ashraf sadraddini* 2; amir hossein nazemi2; Reza delearhasannia3; ajdar onnabi milani4
1PhD. Candidate, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran.
2Professor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran.
3Associate Prof., Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran.
4Assistant Prof., Agricultural and Natural Resources Research and Education Centre, Agricultural Research, Education, and Extension Organization (AREEO), Tabriz, Iran.
Abstract
This study aimed to determine the crop coefficient of sugar beet using canopy cover extracted from digital images under different irrigation managements. The crop coefficient and canopy cover were directly measured by water balance and image processing methods, respectively, in 10 days intervals during the growing season. The crop coefficient of sugar beet in three irrigation managements with maximum allowable depletion (MAD) of 40%, 60%, and 80%, was estimated using its regression equation with canopy cover. This was modeled for potential conditions and then validated by using the average measurements in two years. The findings showed that the estimated crop coefficients were in good agreement with the observations in irrigation managements that had MAD of 40% and 60%. The coefficient of determination (R2), normalized Root Mean Square Error (nRMSE), and model efficiency (EF) were 0.95, 0.11 and 0.95, for 40% MAD, 0.9, 0.13 and 0.85 for 60% MAD, respectively. The results illustrate that the crop coefficient of sugar beet, within the moisture range between field capacity to a MAD of 60%, can be reliably estimated by this approach. The values of determination coefficient (R2), normalized Root Mean Square Error (nRMSE) and model efficiency (EF) decreased to 0.49, 0.37 and 0.63, respectively, for 80% MAD, indicating poor performance of the model under severe drought stress conditions. The proposed method has some advantages including easy and fast data collection, greater accuracy and lower cost, the ability to provide the desired number of images, and no need for meteorological data. Therefore, this can be applied to study the plant growth and crop coefficient variations during the growth period.
Keywords
: Canopeo; Color space; Lysimetric; Maximum Allowable Depletion; Excessive green index
References
  1. بافکار، ع.، فرهادی، ب، و کریمی، ع، ر، 1392. برآورد ضریب گیاهی ذرت دانه­ای با استفاده از خصوصیات فیزیولوژیکی گیاه )مطالعه موردی: ماهیدشت کرمانشاه). نشریه آب و خاک )علوم و صنایع کشاورزی(، جلد 27 ، شماره ص 834-832.
  2. چگینی، م، ع و غالبی، س، 1387. گزارش نهایی تعیین ضریب تعرق گیاهی در مراحل مختلف رشد چغندر بذری. مؤسسه تحقیقات اصلاح و تهیه بذر چغندرقند. کرج.
  3. رئوف، م، 1398. تعیین ضریب گیاهی چغندرقند با استفاده از لایسیمتر در دشت اردبیل و مقایسه آن با داده های جهانی فائو. پژوهش آب در کشاورزی (علوم خاک و آب).
  4. Adams, J.E. and G.F. Arkin. 1977. Alight interception method for measuring row crop ground cover. Soil Sci. Soc. Amer. J. 41:789–792.
  5. Akanda, A.R., M.S. Rahman, M.S. Islam and A.J. Mila. 2017. Crop coefficient of a popular potato variety in Bangladesh. Bangladesh J. Agril. Res. 42(1): 67-76.
  6. Alivernini A, Fares S, Ferrara C, Chianucci F. 2018. An objective image analysis method for estimation of canopy attributes from digital cover photography. Trees. 32(3):713-723.
  7. Allen RG, Pereira LS, Raes D, Smith M. 1998. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations. 56(97): e156.
  8. Allen, R.G. 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J. Hydrol. 229: 27–
  9. Andarzian, ‌B, M. Bannayan, P. Steduto, H. Mazraeh, M. E. Barati, M. A. Barati, and A. Rahnama. "Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran." Agricultural Water Management 100, no. 1 (2011): 1-8.
  10. Blasco J, Aleixos N, Roger J, Rabatel G, Moltó E. 2002. AE-Automation and emerging technologies: Robotic weed control using machine vision. Biosystems Engineering. 83(2):149-157.
  11. Calera A, Martínez C, Melia J. 2001. A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. International Journal of Remote Sensing. 22(17):3357-3362.
  12. Campillo, C., Prieto, M. H., Daza, C., Monino, M. J., & Garcia, M. I. (2008). Using digital images to characterize canopy coverage and light interception in a processing tomato crop. Hortscience, 43(6), 1780-1786.
  13. Carvalho, D.F.D., D. G. D. Silva, H. S. D. Rocha, W. S. D. Almeida, E. D. S. Sousa. 2013. Evapotranspiration and crop coefficient for potato in organic farming. Engenharia Agricola 33(1):201-211.
  14. Chen, J.C., Chang, N.B., and Shieh, W.K. 2003. Assessing wastewater reclamation potential by neural network model. Journal of Engineering Application of Artificial Intelligence, 16:149-157.
  15. Chianucci F, Cutini A. 2013. Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography. Agricultural and Forest Meteorology. 168:130-139.
  16. De Medeiros GA, Arruda FB, Sakai El, Fujiwara M. 2001. The influence of crop canopy on evapotranspiration and crop coefficient of beans (Phaseolus vulgaris L.). Agricultural Water Management. 49(3):211-224.
  17. Fernández-Pacheco DG, Escarabajal-Henarejos D, Ruiz-Canales A, Conesa J, Molina-Martínez JM. 2014. A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain. Biosystems engineering. 117:23-34.
  18. Giacomelli, G.A., Ling, P.P. and Morden, R.E. (1996). An automated plant monitoring system using machine vision. International Symposium on Plant Production in Closed Ecosystems. 440:377-382.
  19. Giacomelli G, Ling P, Kole J. 1998. Determining nutrient stress in lettuce plants with machine vision technology. HortTechnology. 8(3):361-365.
  20. Gime´nez, C. 1985. Resistencia a sequı´a de cultivares de girasol bajo condiciones de campo. Tesis Escuela Superior De Ingenieros Agro´nomos.Universidad de Cordoba.
  21. Grattan S, Bowers W, Dong A, Snyder R, Carroll J, George W. 1998. New crop coefficients estimate water use of vegetables, row crops. California agriculture. 52(1):16-21.
  22. Howell, J. A; L. H. ziska; M.C. Conmik; R. L. Burtch; B. B. Fisher .1987. Response of sugar Beet to lrrigation frequency and off cut on clayloum soil, Irrig – sci, 8:1- 10.
  23. Jamieson, P., Porter, J., and Wilson, D. 1991. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research 27: 337-350.
  24. Korhonen L, Heikkinen J. 2009. Automated analysis of in situ canopy images for the estimation of forest canopy cover. Forest Science. 55(4):323-334.
  25. Kvet, J. and J.K. Marshall. 1971. Assessments of leaf area and other assimilating plant surfaces. Z. sesta´k jc, and p.g. jarvis, The Hague, The Netherlands. p. 517–574.
  26. Laliberte A, Rango A, Herrick J, Fredrickson EL, Burkett L. 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments. 69(1):1-14.
  27. Lee K-J, Lee B-W. 2011. Estimating canopy cover from color digital camera image of rice field. Journal of Crop Science and Biotechnology. 14(2):151-155.
  28. Ling, P.P. and Ruzhitsky, V.N. (1992). Transplant uniformity inspection using machine vision. International Symposium on Transplant Production Systems 319:607-612.
  29. Loague, K., and Green, R. E. 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. Contaminant Hydrology Journal 7: 51-73.
  30. Mora M, Avila F, Carrasco-Benavides M, Maldonado G, Olguín-Cáceres J, Fuentes S. 2016. Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Computers and Electronics in Agriculture. 123:195-202.
  31. Parsons NR, Edmondson R, Song Y. 2009. Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses. Biosystems engineering. 104(2):161-168.
  32. Patrignani A, Ochsner TE. 2015. Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal. 107(6):2312-2320.
  33. Pekin B, Macfarlane C. 2009. Measurement of crown cover and leaf area index using digital cover photography and its application to remote sensing. Remote Sensing. 1(4):1298-1320.
  34. Philipp I, Rath T. 2002. Improving plant discrimination in image processing by use of different colour space transformations. Computers and electronics in agriculture. 35(1):1-15.
  35. Piayda A, Dubbert M, Werner C, Correia AV, Pereira JS, Cuntz M. 2015. Influence of woody tissue and leaf clumping on vertically resolved leaf area index and angular gap probability estimates. Forest Ecology and Management. 340:103-113.
  36. Piccinni, G., J. KO, T. Marek and T. Howell. 2009. Determination of growth– stage–specific crop coefficients (Kc) of maize and sorghum. Agricultural water management 96:1698-1704.
  37. Raabe K, Pisek J, Lang M, Korhonen L. 2017. Estimating the beyond-shoot foliage clumping at two contrasting points in the growing season using a variety of field-based methods. Trees. 31(4):1367-1373.
  38. Raziei, T., & Pereira, L.S. (2013). Spatial variability analysis of reference evapotranspiration in Iran utilizing fine resolution gridded datasets. Agricultural Water Management, 126, 104-118.
  39. Rinaldi, M., and A. V. Vonella, 2006: The response of autumn and spring sown sugar beet (Beta vulgaris L.) to irrigation in Southern Italy: water and radiation use efficiency. Field Crops Res. 95, 103—114.
  40. Ryu Y, Nilson T, Kobayashi H, Sonnentag O, Law BE, Baldocchi DD. 2010. On the correct estimation of effective leaf area index: Does it reveal information on clumping effects? Agricultural and Forest Meteorology. 150(3):463-472.
  41. Salas-Aguilar V, Sánchez-Sánchez C, Rojas-García F, Paz-Pellat F, Valdez-Lazalde JR, Pinedo-Alvarez C. 2017. Estimation of vegetation cover using digital photography in a regional survey of central Mexico. Forests. 8(10):392.
  42. Story D, Kacira M, Kubota C, Akoglu A, An L. 2010. Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and electronics in agriculture. 74(2):238-243.
  43. Uçan, K., & GENÇOĞLAN, C. (2004). The effect of water deficit on yield and yield components of sugar beet. Turkish Journal of Agriculture and Forestry, 28(3), 163-172.
  44. Wang, P., J. Qiu, Z. Huo, M.C. Anderson, Y. Zhou, Y. Bai, et. al. 2017. Temporal downscaling of crop coefficients for winter wheat in the North China Plain: A case study at the Gucheng Agro-Meteorological experimental station. Water 9(155): 1-16.
  45. Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE. 38(1):259-269.
  46. Xiong Y, West C, Brown C, Green P. 2019. Digital image analysis of old world bluestem cover to estimate canopy development.
  47. Zheng X, Wang X. 2010. Leaf vein extraction based on gray-scale morphology. International Journal of Image, Graphics and Signal Processing. 2(2):25.
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