Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agriculture applications due to its potential in precision agriculture such as the identification of weeds and crop production issues, diagnosing nutrient deficiencies, and the prediction of biomass and yield. This study reports on the development for estimating the growth parameters of red pepper. The red pepper field using UAV was studied, and image analysis technologies were studied to develop growth estimation model from acquired images. We used the normalized difference vegetation index (NDVI) that reflects the crop conditions, and vegetation fraction (VF) for 2 major cultivation regions from 2016 to 2017. For this study, UAV imagery was taken at Goesan and Jeongeup regions ten times from June to September during the red pepper growing season. Two plant growth parameters, plant height (P.H.) and fruit set (F.S.) were measured for ten plants per plot for each field campaign. A multiple linear regression model was carried out by using the NDVI and VF extracted from UAV image. As a result, in the case of the P.H. the coefficient of determination was 0.805 and the root mean squared error of measurement was 8.140 cm. And for the F.S., the coefficient of determination was 0.979 and the root mean squared error of measurement was 2.407. These results will also be useful for determining the UAV imagery necessary to estimate growth parameters of red pepper.
Growth estimation model of red pepper using UAV.
Regression equation RMSE R R2 R2adj D.W. P.H. = 438.923NDVIUAV + 121.841VFUAV ‒ 174.651NDVIUAVVFUAV ‒ 160.231 8.140 0.897 0.805 0.708 1.669 △F.S. = 1.994△P.H. ‒ 89.321△NDVIUAV ‒ 17.406 2.407 0.989 0.979 0.968 2.842
P.H.: Plant Height, F.S.: Fruit Set, △: Variation by growing stage.
- Agricultural Weather Information Service Homepage. http://weather.rda.go.kr/ Accessed 5 Aug. 2017.
- Becker-Reshef, I., E. Vermote, M. Lineman, and C. Justice, 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sens. Environ. 114: 1312-1323.10.1016/j.rse.2010.01.010
- Doraiswamy, P.C, T.R. Sinclair, S. Hollinger, B. Akhmedov, A. Stern, and J. Prueger, 2005. Application of MODIS derived parameters for regional crop yield assessment, Remote Sens. Environ. 97:192-202.10.1016/j.rse.2005.03.015
- Hong, S.Y., J.N. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim, 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea, Remote Sens. Environ. 28(5):509-520 (in Korean with English abstract).
- Jeong, S.J., 2018. Development of image preprocessing techniques and fresh weight estimation models for onion (Allium cepa) and garlic (Allium sativum) using UAV-image sensors, Master Thesis, Seoul National University, Seoul, Korea.
- Juan I.C., F.O. Jose, H. David, and A.M. Miguel, 2013. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle, Biosys. Engineer. 115(1):31-42.10.1016/j.biosystemseng.2013.02.002
- Kim, D.W., H.S. Yun, S.J. Jeong, Y.S. Kwon, S.G. Kim, W.S. Lee, and H.J. Kim, 2018. Modeling and testing of growth status for chinese cabbage and white radish with UAV-based RGB imagery, Remote Sens. 10(4):563-588.10.3390/rs10040563
- Korean Statistical Information Service Homepage. http://www.kosis.kr/ Accessed 9 Aug. 2018.
- Lee, K.D., Y.E. Lee, C.W. Park, and S.I. Na. 2016. A comparative study of image classification method to classify onion and garlic using Unmanned Aerial Vehicle (UAV) imagery, Korean J. Soil Sci. Fert. 49(6):743-750 (in Korean with English abstract).10.7745/KJSSF.2016.49.6.743
- Lee, Y.S., J.H. Han, E.S. Han, H.Y. Lee, J.Y. Kim, and B.I. Ahn, 2013. Distribution and consumer's purchase patterns of chili pepper and garlic, Korean Rural Economic Institute, C2013-47:1-2..
- Na, S.I., J.H. Park, and J.K. Park, 2012. Development of Korean paddy rice yield prediction model (KRPM) using meteorological element and MODIS NDVI, J. Korean Soc. Agric. Engineers. 54(3):141-148 (in Korean with English abstract).10.5389/KSAE.2012.54.3.141
- Na S.I., S.C. Baek, S.Y. Hong, K.D. Lee, and G.C. Jang, 2015. A study on the application of UAV for the onion and garlic growth monitoring, KSSSF spring conference (in Korean with English abstract).
- Na, S.I., S.Y. Hong, C.W. Park, K.D. Kim, and K.D. Lee, 2016. Estimation of highland kimchi cabbage growth using UAV NDVI and agro-meteorological factors, Korean J. Soil Sci. Fert. 49(5):420-428 (in Korean with English abstract).10.7745/KJSSF.2016.49.5.420
- Na, S.I., C.W. Park, K.H. So, J.M. Park, and K.D. Lee, 2017a. Monitoring onion growth using UAV NDVI and meteorological factors, Korean J. Soil Sci. Fert. 50(4):306-317 (in Korean with English abstract).10.7745/KJSSF.2017.50.4.306
- Na, S.I., B.K. Min, C.W. Park, K.H. So, J.M. Park, and K.D. Lee, 2017b. Development of field scale model for estimating garlic growth based on UAV NDVI and meteorological factors, Korean J. Soil Sci. Fert. 50(5): 422-433 (in Korean with English abstract).
- Ren J.Q., Z.X. Chen, Q.B. Zhou, and H.J. Tang, 2008. Regional yield estimation for winter wheat with MODIS NDVI data in Shandong, China, Int. J. Appl. Earth Obs. Geoinf. 10:403-413.10.1016/j.jag.2007.11.003
- Rocio B., F.O. Jose, H. David, and A.M. Miguel, 2018. Onion biomass monitoring using UAV based RGB imaging, Preci. Agric. 1-18.
- Rojas, O., 2007. Operational maize yield model development and validation based on remote sensing and agro- meteorological data in Kenya, Int, J. Remote Sens. 28(17):3775-3793.10.1080/01431160601075608
- Publisher :Korean Society of Soil Science and Fertilizer
- Publisher(Ko) :한국토양비료학회
- Journal Title :Korean Journal of Soil Science and Fertilizer
- Journal Title(Ko) :한국토양비료학회 학회지
- Volume : 51
- No :4
- Pages :471-481
- Received Date :2018. 09. 17
- Accepted Date : 2018. 12. 05