Summary of top 4 papers
Recent studies have demonstrated the potential of Unmanned Aerial Vehicles (UAVs) in estimating sugarcane yield. Xu (2020) used LiDAR data from a UAV to estimate aboveground fresh weight, with the best results achieved using a random forest regression model. Shi (2018) improved yield estimation by assimilating UAV-derived leaf area index and ground-measured soil water content into a crop model. Souza (2017) utilized a UAV with an RGB camera to estimate sugarcane height, a key indicator of yield, and found that the method was accurate when compared to ground references. Lastly, Som-ard (2018) used UAV-acquired RGB images and ground data to estimate sugarcane yield, achieving high accuracy with an object-based image analysis technique. These studies collectively highlight the potential of UAVs in providing accurate and timely estimations of sugarcane yield, which can significantly benefit the sugar industry.
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