Measurement of the banana pseudo-stem phenotypic parameters based on ellipse model

Yinlong Jiang, Jieli Duan, Xing Xu, Yunhe Ding, Yang Li, Zhou Yang

Abstract


The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas, and it can provide essential data support for mechanized cultivation operations such as fertilization and pesticide application. Existing studies mainly measure the diameter of banana pseudo-stem as its phenotypic parameter. The banana pseudo-stem cross section was closer to an ellipse other than a standard circle, so the diameter parameter cannot adequately represent the phenotypic characteristics of the banana plant. In this study, an automatic measuring device for banana pseudo-stem phenotypic parameters was developed. The device, which integrates three different types of sensors: a laser ranging sensor, a rotary encoder, and a digital camera, was used to obtain the point cloud and image data of banana pseudo-stem. A K-means point clouds clustering algorithm based on Euclidean distance was proposed. The point cloud of banana pseudo-stem was identified and extracted. A three-dimensional reconstruction algorithm based on the ellipse model was also proposed. The three-dimensional contour of the pseudo-stem was calculated to obtain three types of phenotypic parameters: the long axis length, the short axis length, and the perimeter. Further, a synchronous trigger image acquisition mechanism was used to take pictures of pseudo-stems during measurement. It can be utilized for manual assessment of the growth status of the banana. Field experimental results showed that the three banana phenotypic parameters had a high correlation with the manual measurement results, and R2 is always more significant than 0.95, the total average measurement error and relative error were only 6.16 mm and 4.38%, respectively, both are within the acceptable agronomy range. In general, this method has good universality for plant stem detection, and the stem phenotypic parameters can be obtained by means of a non-contact test, which is of great significance to the mechanized cultivation of the forest and fruit industry.
Keywords: multi-sensor fusion, point cloud fitting, phenotypic parameter extraction, banana pseudo-stem, ellipse model
DOI: 10.25165/j.ijabe.20221503.6614

Citation: Jiang Y L, Duan J L, Xu X, Ding Y H, Li Y, Yang Z. Measurement of the banana pseudo-stem phenotypic parameters based on ellipse model. Int J Agric & Biol Eng, 2022; 15(3): 195–202.

Keywords


multi-sensor fusion, point cloud fitting, phenotypic parameter extraction, banana pseudo-stem, ellipse model

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References


Fu L H, Duan J L, Zou X J, Lin G C, Song S S, Ji B, et al. Banana detection based on color and texture features in the natural environment. Computers and Electronic in Agriculture, 2019; 167: 105057. doi: 10.1016/j.compag.2019.105057.

Zhao L, Yang H J, Xie H, Duan J L, Jin M H, Fu H, et al. Effects of morphological and anatomical characteristics of banana crown vascular bundles on cutting mechanical properties using multiple imaging methods. Agronomy, 2020; 10(8): 1199. doi: 10.3390/agronomy10081199.

Food and Agriculture Organization of the United Nations (FAO). Production/yield quantities of bananas in the world. Available: http://www.fao.org/faostat/en/#data/QC/visualize. Accessed on [2021-02-05].

Li Y P, Fang J, Dong D G, Liang W H, Liu Y Q, Gu X L. Analysis on the development status and trend of the world banana industry. Guangdong Agricultural Sciences, 2008; 2: 115-119. (in Chinese)

Guo J, Duan J L, Li J, Yang Z. Mechanized technology research and equipment application of banana post-harvesting: A review. Agronomy, 2020; 10(3): 374. doi: 10.3390/agronomy10030374.

Silva G J, Scapin M D, Silva F P, Silva A R P, Behlau F, Ramos H H. Spray volume and fungicide rates for citrus black spot control based on tree canopy volume. Crop Protection, 2016; 85: 38–45.

Zhang P, Deng L, Lyu Q, He S L, Yi S L, Liu Y D, et al. Effects of citrus tree-shape and spraying height of small unmanned aerial vehicle on droplet distribution. Int J Agric & Biol Eng, 2016; 9(4): 45–52.

Xia Z, Xu J B, Wang Z K, Song L S. Development and application of fertilization information systems based on ArcEngine. In: 2011 International Conference on Electric Information and Control Engineering, Wuhan: IEEE, 2011; pp.4136–4139. doi: 10.1109/ICEICE.2011.5778158.

Manandhar A, Zhu H P, Ozkan E, Shah A. Techno-economic impacts of using a laser-guided variable-rate spraying system to retrofit conventional

constant-rate sprayers. Precision Agriculture, 2020; 21: 1156–1171.

Palleja T, Landers A J. Real time canopy density estimation using ultrasonic envelope signals in the orchard and vineyard. Computers and Electronics in Agriculture, 2015; 115: 108–117.

Palleja T, Landers A J. Real time canopy density validation using ultrasonic envelope signals and point quadrat analysis. Computers and Electronics in Agriculture, 2017; 134: 43–50.

Berk P, Stajnko D, Belsak A, Hocevar M. Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system. Computers and Electronics in Agriculture, 2020; 169: 105158. doi: 10.1016/j.compag.2019.105158.

Llop J, Gil E, Llorens J, Miranda F A, Gallart M. Testing the suitability of a terrestrial 2D LiDAR scanner for canopy characterization of greenhouse tomato crops. Sensors, 2016; 16(9): 1435. doi: 10.3390/s16091435.

Asaei H, Jafari A, Loghavi M. Site-specific orchard sprayer equipped with machine vision for chemical usage management. Computers and Electronics in Agriculture, 2019; 162: 431–439.

Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, et al. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. GigaScience, 2019; 8(1): giy153. doi: 10.1093/gigascience/giy153.

Song S S, Duan J L, Yang Z, Zou X J, Fu L H, Ou Z W. A three-dimensional reconstruction algorithm for extracting parameters of the banana pseudo-stem. Optik, 2019; 185: 486–496.

Xu X, Zhang Z H, Yang Z, Xu Y, Guo J, Chen Y F, et al. Design of semi-automatic banana bud removal machine. IFAC-PapersOnLine, 2018; 51(17): 146–151.

Thalheimer M. A new optoelectronic sensor for monitoring fruit or stem radial growth. Computers and Electronics in Agriculture, 2016; 123: 149–153.

Che J, Zhao C, Zhang Y, Wang C, Qiao X, Zhang X. Plant stem diameter measuring device based on computer vision and embedded system. In: 2010 World Automation Congress, 2010; pp.51–55.

Bao Y, Tang L, Breitzman M W, Fernandez M G S, Schnable P S. Field-based robotic phenotyping of sorghum plant architecture using stereo vision. Journal of Field Robotics, 2019; 36(2): 397–415.

Quan L Z, Chen C, Li Y J, Qiao Y J, Xi D J, Zhang T Y, et al. Design and test of stem diameter inspection spherical robot. Int J Agric & Biol Eng, 2019; 12(2): 141–151.

Ye W F, Qian C, Tang J, Liu H, Fan X Y, Liang X L, et al. Improved 3D stem mapping method and elliptic hypothesis-based DBH estimation from terrestrial laser scanning data. Remote Sensing, 2020; 12(3): 352. doi: 10.3390/rs12030352.

Liu T H, Reza E, Arash T, Zou X J, Wang H J. Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model. Computers in Industry, 2018; 99: 9–16.

Julio C P, Thomas R. Novel image processing approach for solving the overlapping problem in agriculture. Biosystems Engineering, 2013; 115(1): 106–115.

Fitzgibbon A W, Pilu M, Fisher R B. Direct least squares fitting of ellipses. In: Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria: IEEE, 1996; pp.253–257. doi: 10.1109/ ICPR.1996.546029.




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