Abstract
Precision agriculture is improving agriculture management worldwide through technologies such as global navigation satellite system (GNSS), robotics, sensors, and variable rate applications. Geographic information system (GIS) and remote sensing are fundamental techniques for precision agriculture, providing different types of information: plantation layouts, crop health, and plant growth stages; these tools can provide information to farmers quickly. The popularization of unmanned aerial vehicles (UAV) made those aircraft more affordable and easy to use, providing information with high spatial and temporal resolutions. This study aimed to predict corn crop yield through corn height estimation generated by 3D photogrammetry based on structure from motion technology. The UAV data were taken in 14-flight campaign to acquire 3D; red, green, and blue (RGB); and normalized difference vegetation index (NDVI) data for 5 months in 2017 and compared with the ground data obtained in the harvest in middle October of the same year. The methodology allows understanding the whole field, while other methods are based on sample data, showing it to be more convenient since it is less time-consuming. Considering only the UAV height estimation (UHE) variable, the prediction reached an R-squared value of 0.51 with dry grain yield at the beginning of August and allowed plant height monitoring after NDVI saturation, presenting a high potential for yield prediction and crop monitoring.
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Furukawa, F., Maruyama, K., Saito, Y.K., Kaneko, M. (2020). Corn Height Estimation Using UAV for Yield Prediction and Crop Monitoring. In: Avtar, R., Watanabe, T. (eds) Unmanned Aerial Vehicle: Applications in Agriculture and Environment. Springer, Cham. https://doi.org/10.1007/978-3-030-27157-2_5
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