publication . Article . 2020

Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV

Predrag Ranđelović; Vuk Đorđević; Stanko Milić; Svetlana Balešević-Tubić; Kristina Petrović; Jegor Miladinović; Vojin Đukić;
Open Access English
  • Published: 31 Jul 2020 Journal: Agronomy, volume 10, issue 8, page 1,108 (eissn: 2073-4395, Copyright policy)
<jats:p>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 th...
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free text keywords: soybean, machine learning, vegetation indices, UAV, RGB images, Mean absolute error, Vegetation, Mean squared error, Correlation coefficient, RGB color model, Mathematics, computer.software_genre, computer, Artificial intelligence, business.industry, business, Model validation, Plant density, lcsh:Agriculture, lcsh:S
Funded by
Increasing the efficiency and competitiveness of organic crop breeding
  • Funder: European Commission (EC)
  • Project Code: 771367
  • Funding stream: H2020 | RIA
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