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http://dx.doi.org/10.7780/kjrs.2021.37.6.1.15

Evaluation of Applicability of RGB Image Using Support Vector Machine Regression for Estimation of Leaf Chlorophyll Content of Onion and Garlic  

Lee, Dong-ho (Department of Agricultural and Rural Engineering, Chungbuk National University)
Jeong, Chan-hee (Department of Agricultural and Rural Engineering, Chungbuk National University)
Go, Seung-hwan (Department of Agricultural and Rural Engineering, Chungbuk National University)
Park, Jong-hwa (Department of Agricultural and Rural Engineering, Chungbuk National University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.6_1, 2021 , pp. 1669-1683 More about this Journal
Abstract
AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R2 of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.
Keywords
leaf chlorophyll content; SVM regression; RGB; multi-spectral sensor; drone; vegetation index;
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