Comparative analysis of feature selection techniques to identify a set of optimal features for crop yield estimation using UAS-based multisensor data

Accurate crop yield estimation is essential for decision-making and planning in modern global agriculture, including in the USA, and is becoming increasingly critical for sustainable productivity to tackle climate change and food security challenges. Yield estimation provides farmers with insights into potential yields, enabling optimized resource allocation (water, fertilizer, pesticides, etc.), refined agricultural management strategies, and enhanced overall profitability. This research focused on feature selection for yield estimation of two of the top six crops in Mississippi and the US: corn and cotton. The study was conducted over a 2.31-hectare field encompassing both crop fields at the R. R. Foil Plant Science Research Center in Mississippi, US. The experiment took place over four consecutive years (2020 ∼ 2023) during the crop growth period, from late April to mid-October each year. Weekly data were collected utilizing multispectral cameras and LiDAR sensors mounted on unmanned aircraft systems (UAS). Soil moisture (SM) map and soil temperature data were obtained from volumetric soil moisture probes, while all weather and environmental parameters were sourced from a nearby field weather station. The dataset for each plot consisted of over 30 features across five major categories, with ground truth yield data collected for 235 plots (30 cotton and 30 corn plots) over the 4-year period. The investigation of feature selection techniques included Pearson’s correlation coefficient filtering, recursive feature elimination wrapping, and recursive groupwise wrapping, with the aim of identifying the most relevant features for yield estimation. For performance evaluation, crop yield prediction was conducted using a Long Short-Term Memory (LSTM) network, with root mean square error (RMSE) used as the performance metric. Among the methods, recursive groupwise wrapping produced comparatively lower RMSEs, indicating superior performance over selected weeks. Ultimately, the feature selection process enhances crop yield estimation accuracy compared to the usage of all available features.