Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV
2020
Аутори
Ranđelović, PredragĐorđević, Vuk
Milić, Stanko
Balešević-Tubić, Svetlana
Petrović, Kristina
Miladinović, Jegor
Đukić, Vojin
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants ...per unit area (m(2)). Model validation resulted in the correlation coefficient-R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor.
Кључне речи:
soybean / machine learning / vegetation indices / UAV / RGB imagesИзвор:
Agronomy-Basel, 2020, 10, 8Издавач:
- Basel : MDPI
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200032 (Научни институт за ратарство и повртарство, Нови Сад) (RS-MESTD-inst-2020-200032)
- ECOBREED (Increasing the efficiency and competitiveness of organic crop breeding) (EU-H2020-771367)
DOI: 10.3390/agronomy10081108
ISSN: 2073-4395
WoS: 000567098400001
Scopus: 2-s2.0-85091698647
Колекције
Институција/група
FiVeRTY - JOUR AU - Ranđelović, Predrag AU - Đorđević, Vuk AU - Milić, Stanko AU - Balešević-Tubić, Svetlana AU - Petrović, Kristina AU - Miladinović, Jegor AU - Đukić, Vojin PY - 2020 UR - http://fiver.ifvcns.rs/handle/123456789/2046 AB - Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m(2)). Model validation resulted in the correlation coefficient-R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor. PB - Basel : MDPI T2 - Agronomy-Basel T1 - Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV IS - 8 VL - 10 DO - 10.3390/agronomy10081108 ER -
@article{ author = "Ranđelović, Predrag and Đorđević, Vuk and Milić, Stanko and Balešević-Tubić, Svetlana and Petrović, Kristina and Miladinović, Jegor and Đukić, Vojin", year = "2020", abstract = "Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m(2)). Model validation resulted in the correlation coefficient-R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor.", publisher = "Basel : MDPI", journal = "Agronomy-Basel", title = "Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV", number = "8", volume = "10", doi = "10.3390/agronomy10081108" }
Ranđelović, P., Đorđević, V., Milić, S., Balešević-Tubić, S., Petrović, K., Miladinović, J.,& Đukić, V.. (2020). Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV. in Agronomy-Basel Basel : MDPI., 10(8). https://doi.org/10.3390/agronomy10081108
Ranđelović P, Đorđević V, Milić S, Balešević-Tubić S, Petrović K, Miladinović J, Đukić V. Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV. in Agronomy-Basel. 2020;10(8). doi:10.3390/agronomy10081108 .
Ranđelović, Predrag, Đorđević, Vuk, Milić, Stanko, Balešević-Tubić, Svetlana, Petrović, Kristina, Miladinović, Jegor, Đukić, Vojin, "Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV" in Agronomy-Basel, 10, no. 8 (2020), https://doi.org/10.3390/agronomy10081108 . .