The flowering date of sunflowers is a crucial trait that significantly influences crop management practices and product placement. This trait can be measured by counting the number of days from planting until 50% of plants in a given research plot have reached flowering at R5 developmental growth stage. Traditional ground methods for data collection are labor-intensive and subjective, requiring field scientists to manually estimate and record plots with 50% flowering every 1-2 days. This approach not only consumes considerable time but also potentially overlooks valuable information related to flowering rates and duration. To address these challenges, we leveraged UAVs (Unmanned Aerial Vehicles), which allow for surveying a field in a short span of time, to model flower counts over time and predict the date when the plot reached 50% flowering. The method developed employs a deep learning model trained to detect yellow sunflower heads from UAV imagery and modeling these counts over time using a logistic function to estimate the 50% flowering date. With this method, flowering date was precisely estimated with high correlation relative to the ground measurements (r = >0.92) across experiments and locations. An increase in heritability for the remote sensing trait was also observed relative to the ground trait, and more importantly we were able to gain additional insights into flowering rates and duration. This innovative approach offers a promising avenue for enhancing the efficiency and accuracy of sunflower phenotyping.

A dry beans (Phaseolus vulgaris L.) cultivar must fit the environment in which it will be grown. Therefore, days to maturity (DM) is the most important physiological component affecting yield and grain quality outcomes. Additionally, dry bean stand count (SC) at early growth stages estimation provides useful information for agronomic decision-making and can measure root rot loss due to damping-off. The visual inspection to determine the accurate maturity date and the final number of emerged plants is labor-intensive, time-demanding, and tedious. Therefore, there is an increasing demand for alternative approaches to estimating DM and SC in a high-throughput phenotyping mode (HTP). In this study, we developed a Deep Learning (DL) HTP pipeline to capture the sequential behavior of time series data for estimating DM and to identify target plants in the early growth stage for SC estimation using field dry bean data obtained from aerial RGB images at the plot-level. A state-of-the-art hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) was used to extract DM features and capture the sequential behavior of time series data. Faster R-CNN object detection method was deployed to SC. The DL model to estimate DM was tested on five different environments across years, and SC was done comparing different ground sample resolutions in two trials. Results suggest the effectiveness of the CNN-LSTM and Faster R-CNN models employed compared to traditional methods. Furthermore, this study highlighted the technical parameters that can influence the DL model results in the breeding program decision-making.