ارزیابی یکنواختی کاشت نشاء ذرت توسط دستگاه نشاءکار با روش پردازش تصویر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان

2 دانشکده مهندسی‌ زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان

چکیده

Abstract
Cultivation of corn in the form of seedlings can be considered as a solution for producing early crops, reducing production time, and saving water and various agricultural inputs. Therefore, the aim of the current research was to develop an algorithm to evaluate the uniformity of planting in the nursery with the help of image processing. In order to determine the number of seedlings, various transformations (excessive greenness, normalized intensity difference index of red and green values ​​and normalized intensity difference index of blue and green values) were evaluated. Then, by applying thresholding by Etsu method, the information of corn seedlings was separated from the background. Finally, by implementing the Freeman labeling method and the Harris method and analyzing the features of overlapping areas, an automatic algorithm was developed to count corn seedlings in these areas. The developed algorithm was evaluated online at speeds of 5, 8, 10 and 12 km/h. According to the obtained results, the best uniformity and monoculture was obtained by the transplanter at a speed of 5 km/h with a percentage of correct cultivation of 97% and the highest percentage of incorrect cultivation at a speed of 12 km/h and equal to 21%. Therefore, based on the present study, according to the obtained accuracy, the developed algorithm can be used not only to count the number of corn seedlings in the field, but also to evaluate the uniformity of planting of seedlings.

کلیدواژه‌ها


عنوان مقاله [English]

Assessment of uniformity of corn seedling planting in transplanted using image processing

نویسندگان [English]

  • Saman Abdanan Mehdizadeh, 1
  • Hadi Orak, 1
  • Fateme Kazemi Karaji 2
1 Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan,
2 Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan
چکیده [English]

Abstract
Cultivation of corn as a seedling could be considered as a way to produce an early crop, reduce production time and also save water and agricultural inputs. Therefore, the aim of the present study is developing an algorithm for evaluation of the planting uniformity in transplanting using image processing. In order to determine the number of seedlings, different transformation methods (Excessive green, green and red normalized difference index and green and blue normalized difference index) were evaluated. Then, the corn seedlings information was taken out from the circumstantial information using Otsu’s method. Finally, by applying Freeman labeling and Harris method and analyzing the characteristic parameters of the overlapping regions of the fields, an automatic counting of corn seedlings in the overlapping regions was established. The developed algorithm was evaluated online at speeds 5, 8, 10 and 12 km / h. According to the obtained results, the best uniformity and single transplanting were achieved at a speed of 5 km / h with an accuracy of 97%. Furthermore, the highest percentage of incorrect planting was obtained 21% at a speed of 12 km / h. Therefore, the study demonstrated that the newly developed algorithm is reliable not only for automatic corn seedlings counting in the corn field but also for the uniformity evaluation of the transplanter.

کلیدواژه‌ها [English]

  • Image processing
  • Digital camera
  • Seedling counting
  • planting map
Anomymous. (2016). Production and trade of basic products of the agricultural sector in the period of 2001-2016. From https://rc.majlis.ir/fa/report/download/1003180 (In Farsi)
Abdolahzare, Z. and Abdanan Mehdizadeh, S. (2018a). Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing. Neural Computing and Applications, 29 (2), 363-375.
Abdolahzare, Z. and Abdanan Mehdizadeh, S. (2018b). Real time laboratory and field monitoring of the effect of the operational parameters on seed falling speed and trajectory of pneumatic planter. Computers and Electronics in Agriculture, 145, 187-198.
Abdanan Mehdizadeh, S. and Abdullah Zare, Z. (2016). Evaluation of pneumatic agent performance based on film processing and Kalman filter. Iranian Biosystem Engineering (Iranian Agricultural Sciences), 47 (2), 363-373. (In Farsi)
Cao, Q. He, M. R. Dai, X. L. Men, H. W. and Wang, C. Y. (2011). Effects of interaction between density and nitrogen on grain yield and nitrogen use efficiency of winter wheat. Plant Nutrition and Fertilizer Science, 17, 815–822.
Dehghanpour, Z. (2014). Technical instructions for planting, holding and harvesting corn (grain and fodder). Agricultural Education Publication, 110 pages. (In Farsi)
Dhupal, G., and Sahu, S. (2020). Fabrication of manual operated two row tomato transplanter. Journal of Pharmacognosy and Phytochemistry, 9 (4), 290-293.
Fanadzo, M. Chiduza, S. and Mnkeni, P. N. (2010). Comparative response of direct seeded and transplanted maize to nitrogen fertilization at Zanyokwe Irrigation Scheme, Eastern Cape, South Africa.
Gai, J. Tang, L. and Steward, B. L. (2020). Automated crop plant detection based on the fusion of color and depth images for robotic weed control. Journal of Field Robotics, 37 (1), 35-52
Gai, J. Xiang, L. and Tang, L. (2021). Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle. Computers and Electronics in Agriculture, 188, 106301.
 
 Heidari, A. Haji Agha Alizadeh, H.  Yazdanpanah, A. Amiri Parian, J. (2016). The effect of different tillage and fertilization methods on soil physical properties and grain yield, Journal of Soil and Water Sciences (Agricultural Science and Technology and Natural Resources), 20 (4), 103-112. (In Farsi).
Imam, Y. (2012). Cereal cultivation. Shiraz University Press. Fourth edition, 194 pages (In Farsi).
Jusoh, N. A. and Zain, J. M. (2009). Application of Freeman Chain codes: An alternative recognition technique for Malaysisn car plates. International Journal of Computer Science and Network Security. 9 (11): 222-227.
Lin, S. Jiang, Y. Chen, X. Biswas, A. Li, S. Yuan, Z. and Qi, L. (2020). Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R-CNN. IEEE Access, 8, 147231-147240.
Mallick, D. K. Ray, R. and Dash, S. R. (2020). Detection and Classification of Crop Diseases from Its Leaves Using Image Processing. In Smart Intelligent Computing and Applications (pp. 215-228), Springer, Singapore.
Meyer, G. E. and Neto, J. C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and electronics in agriculture, 63 (2), 282-293.
Onal, I. Degirmencioglu, A. and Yazgi, A. (2012). An evaluation of seed spacing accuracy of a vacuum type precision metering unit based on theoretical considerations and experiments. Turkish Journal of Agricultural and Forestry, 36, 133-144.
Pan, X. Zhu, J. Yu, H. Chen, L. Liu, Y. and Li, L. (2021). Robust corner detection with fractional calculus for magnetic resonance imaging. Biomedical Signal Processing and Control, 63, 102112.
Ribera, J., Chen, Y., Boomsma, C., and Delp, E. J. (2017). Counting plants using deep learning. In 2017 IEEE global conference on signal and information processing, 1344-1348.
Shirzadifar, A. Maharlooei, M. Bajwa, S. G. Oduor, P. G. and Nowatzki, J. F. (2020). Mapping crop stand count and planting uniformity using high resolution imagery in a maize crop. Biosystems Engineering, 200, 377-390.
Shrestha, D. S. and Steward, B. L. (2003). Automatic corn plant population measurement using machine vision. Transactions of the Asae, 46, 559–566.
Soille, P. Vogt, J. and Colombo, R. (2003). Carving and adaptive drainage enforcement of grid digital elevation models. Water resources research, 39 (12).
Valente, J., Sari, B., Kooistra, L., Kramer, H., and Mücher, S. (2020). Automated crop plant counting from very high-resolution aerial imagery. Precision Agriculture, 21, 1366-1384.
Vantine, M. and Verlinden, S. (2003). Growing organic vegetable transplants. West verginia university.
Xue, Y. F. Zhang, W. Liu, D. Y. Yue, S. C. Cui, Z. L. Chen, X. P. and Zou, C. Q. (2014). Effects of nitrogen management on root morphology and zinc translocation from root to shoot of winter wheat in the field. Field Crops Research, 161, 38-45. ‏and Xie Z. Q. (2010). Detection technology for precision metering performance of magnetic-type seeder based on machine vision. Computer and Computing Technologies in Agriculture IV. 4th IFIP TC 12 conference, CCTA, selected papers, part 1. 555-562.
Yang, D. Hu, J. And Xie, Z. (2011). Detection Technology for Precision Metering Performance of Magnetic-Type Seeder Based on Machine Vision. Computer and Technology in Agriculture IV. CCTA , Vol 344. Springer, Berlin, Heidelberg.
Zhang, L. Weckler, P. Wang, N. Xiao, D. and Chai, X. (2016). Individual leaf identification from horticultural crop images based on the leaf skeleton. Computers and Electronics in Agriculture , 127, 184-196.
Zhang, X. Wang, Y. Zhao, Q. Bao, F. Wu, S. and Yan, P. (2020). Fast automatic multi-defects recognition based on the spatial characteristics of Freeman chain code. Optical Engineering,  59 (12), 124103.