Determination of Plant Height for Crop and Weed Discrimination by Using Stereo Vision System

Yıl 2020, , 97 - 107, 26.01.2020

Ömer Barış Özlüoymak

https://doi.org/10.33462/jotaf.626709

Cited By: 1

Öz

The stereo
vision experiments were conducted under the laboratory conditions by using
LabVIEW programming language. An artificial crop plant and six types of
artificial weed samples were used in the experiments. The information related
to the plant height is a relevant feature to classify the crop plant and weed,
especially in the early growth stage. A binocular stereo vision system was
established by using two identical webcams with parallel optical axes and a
laptop computer to discriminate the artificial crop plant and six types of
weeds correctly. The calculated depth values were compared with the physical
measurements for the same points. While the measurement error of the system was
less than 3.50% for the artificial crop plant, it was less than 4.20% for six
artificial weed samples. There were also strong, positive and significant
linear correlations between the stereo vision and physical height measurements
for artificial crop plant and weed samples. Calculated correlation values (R²)
between the stereo vision and physical height measurements were 0.962 for the
artificial crop plant and 0.978 for the artificial weed samples, respectively.
That stereo vision system could be integrated into automatic spraying systems
for intra-row spraying applications.

Anahtar Kelimeler

3D Depth Measurement, LabVIEW, Spraying, Stereo Vision, Weed

Ürün ve Yabancı Ot Ayrımı için Stereo Görme Sistemi Kullanılarak Bitki Yüksekliğinin Belirlenmesi

Öz

Stereo görme denemeleri, LabVIEW programlama dili kullanılarak
laboratuvar koşullarında yapılmıştır. Denemelerde, yapay bir ürün bitkisi ile
altı tür yapay yabancı ot örneği kullanılmıştır. Bitki yüksekliği ile ilgili
bilgi; özellikle ilk büyüme döneminde, ürün bitkisi ile yabancı otların
sınıflandırılması için önemli bir özelliktir. Yapay ürün bitkisi ile altı tür
yabancı otu doğru şekilde birbirlerinden ayırt etmek için paralel optik eksenli
iki özdeş web kamerası ve bir dizüstü bilgisayar kullanılarak, bir binoküler
stereo görme sistemi geliştirilmiştir. Hesaplanan derinlik değerleri, aynı
noktalardan alınan fiziksel ölçümlerle karşılaştırılmıştır. Sistemin ölçüm
hatası; yapay ürün bitkisi için %3.50'den az iken, altı tane yapay yabancı ot
örneği için %4.20'den az olmuştur. Yapay ürün bitkisi ve yabancı ot örnekleri
için stereo görme ile fiziksel yükseklik ölçümleri arasında; güçlü, pozitif ve
anlamlı doğrusal bir korelasyon vardır. Stereo görme ve fiziksel yükseklik
ölçümleri arasındaki hesaplanan korelasyon değeri (R²), yapay ürün
bitkisi için 0.962; yapay yabancı ot örnekleri için ise 0.978’dir. Bu stereo
görme sistemi, sıra üzeri ilaçlama uygulamaları için otomatik ilaçlama
sistemlerine entegre edilebilir.

Anahtar Kelimeler

3D derinlik ölçümü, LabVIEW, İlaçlama, Stereo Görme, Yabancı Ot

Kaynakça

• Andersen, H.J., Reng, L., Kirk, K. 2005. Geometric plant properties by relaxed stereo vision using simulated annealing. Computers and Electronics in Agriculture, 49, 219–232.
• Birchfield, S., Tomasi, C. 1999. Depth discontinuities by pixel-to-pixel stereo. International Journal of Computer Vision, 35(3), 269–293.
• Gonzalez-de-Soto, M., Emmi, L., Perez-Ruiz, M., Aguera, J., Gonzalez-de-Santos, P. 2016. Autonomous systems for precise spraying-evaluation of a robotised patch sprayer. Biosystems Engineering, 146, 165-182.
• Holonec, R., Copindean, R., Dragan, F., Dan Zahara, V. 2014. Object tracking system using stereo vision and LabVIEW algorithms. Acta Electrotehnica, 55(1-2), 71-76.
• Jafari, A., Mohtasebi, S.S., Jahromi, H.E., Omid, M. 2006a. Weed detection in sugar beet fields using machine vision. International Journal of Agriculture & Biology, 8(5), 602-605.
• Jafari, A., Mohtasebi, S.S., Jahromi, H.E., Omid, M. 2006b. Color segmentation scheme for classifying weeds from sugar beet using machine vision. Iranian Journal of Information Science & Technology, 4(1), 1-12.
• Li, D., Xu, L., Tang, X., Sun, S., Cai, X., Zhang, P. 2017. 3D imaging of greenhouse plants with an inexpensive binocular stereo vision system. Remote Sensing, 9, 508, 1-27.
• Lin, T., Lai, T., Liu, C., Cheng, Y. 2011. A three-dimensional imaging approach for plant feature measurement using stereo vision. Tarım Makinaları Bilimi Dergisi (Journal of Agricultural Machinery Science), 7(2), 153-158.
• Loghavi, M., Mackvandi, B.B. 2008. Development of a target oriented weed control system. Computers and Electronics in Agriculture, 63: 112-118.
• Loni, R., Loghavi, M., Jafari, A. 2014. Design, development and evaluation of targeted discrete-flame weeding for inter-row weed control using machine vision. American Journal of Agricultural Science and Technology, 2(1), 17-30.
• Özlüoymak, Ö.B., Bolat, A., Bayat, A., Güzel, E. 2019. Design, development, and evaluation of a target oriented weed control system using machine vision. Turkish Journal of Agriculture and Forestry, 43, 164-173.
• Piron, A., Van der Heijden, F., Destain, M.F. 2011. Weed detection in 3D images. Precision Agriculture, 12, 607–622.
• Sabancı, K., Aydın, C. 2014. Image processing based precision spraying robot. Journal of Agricultural Sciences, 20, 406-414.
• Sabanci, K., Aydin, C. 2017. Smart robotic weed control system for sugar beet. Journal of Agricultural Science and Technology, 19, 73-83.
• Shirzadifar, A.M., Loghavi, M., Raoufat, M.H. 2013. Development and evaluation of a real time site-specific inter-row weed management system. Iran Agricultural Research, 32(2), 39-54.
• Tellaeche, A., Burgos-Artizzub, X.P., Pajaresa, G., Ribeirob, A. 2008. A vision-based method for weeds identification through the bayesian decision theory. Pattern Recognition Society, 41, 521-530.
• Timmermann, C., Gerhads, R., Kühbauch, W. 2003. The economic impact of site-specific weed control. Precision Agriculture, 4, 249-260.
• Xia, C., Li, Y., Chon, T., Lee, J. 2009. A stereo vision based method for autonomous spray of pesticides to plant leaves Paper presented at the IEEE International Symposium on Industrial Electronics (ISIE 2009), Seoul-Korea, 909-914.
• Xiong, X., Yu, L., Yang, W., Liu, M., Jiang, N., Wu, D., Chen, G., Xiong, L., Liu, K., Liu, Q. 2017. A high‑throughput stereo‑imaging system for quantifying rape leaf traits during the seedling stage. Plant Methods, 13(7), 1-17.
• Yang, C., Prasher, S.O., Landry, J., Kok, R. 2002. A vegetation localization algorithm for precision farming. Biosystems Engineering, 81(2), 137-146.
• Yang, C., Prasher, S.O., Landry, J., Ramaswamy, H.S. 2003. Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture, 4, 5-18.