Physiological maturity date is a critical parameter for the selection of breeding lines in soybean breeding programs. The conventional method to estimate the maturity dates of breeding lines uses visual ratings based on pod senescence by experts, which is subjective by human estimation, labor-intensive and time-consuming. Unmanned aerial vehicle (UAV)-based phenotyping systems provide a high-throughput and powerful tool of capturing crop traits using remote sensing, image processing and machine learning technologies. The goal of this study was to investigate the potential of predicting maturity dates of soybean breeding lines using UAV-based multispectral imagery. Maturity dates of 326 soybean breeding lines were taken using visual ratings from the beginning maturity stage (R7) to full maturity stage (R8), and the aerial multispectral images were taken during this period on 27 August, 14 September and 27 September, 2018. One hundred and thirty features were extracted from the five-band multispectral images. The maturity dates of the soybean lines were predicted and evaluated using partial least square regression (PLSR) models with 10-fold cross-validation. Twenty image features with importance to the estimation were selected and their changing rates between each two of the data collection days were calculated. The best prediction (R2 = 0.81, RMSE = 1.4 days) was made by the PLSR model using image features taken on 14 September and their changing rates between 14 September and 27 September with five components, leading to the conclusion that the UAV-based multispectral imagery is promising and practical in estimating maturity dates of soybean breeding lines.

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HighlightsUAV imagery can be used to characterize newly-emerged corn plants.Size and shape features used in a random forest model are able to predict days after emergence within a 3-day window.Diameter and area were important size features for predicting DAE for the first, second, and third week of emergence.Abstract. Assessing corn (Zea mays L.) emergence uniformity soon after planting is important for relating to grain production and making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been used for determining corn densities at vegetative growth stage 2 (V2) and later, but not as a tool for quantifying emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery and a machine learning method. A field experiment was designed with four planting depths to obtain a range of corn emergence dates. UAV imagery was collected during the first, second, and third weeks after emergence. Acquisition height was approximately 5 m above ground level, which resulted in a ground sampling distance of 1.5 mm pixel-1. Seedling size and shape features derived from UAV imagery were used for DAE classification based on a random forest machine learning model. Results showed that 1-day DAE could be distinguished based on image features within the first week after initial corn emergence with a moderate overall classification accuracy of 0.49. However, for the second week and beyond, the overall classification accuracy diminished (0.20 to 0.35). When estimating DAE within a 3-day window (-1 to +1 day), the overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and the ratio of major axis length to area were important image features to predict corn DAE. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date to assist in assessing emergence uniformity. Additional studies are needed for fine-tuning the image collection procedures and image feature identification to improve accuracy. Keywords: Corn emergence, Image features, Random forest, Unmanned aerial vehicle.