Soybean Maturity Prediction using 2D Contour Plots from Drone based Time Series Imagery

TL;DR

This paper presents a deep learning approach using UAV-derived 2D contour plots from time-series imagery to accurately and efficiently predict soybean maturity, reducing reliance on subjective manual assessments in breeding programs.

Contribution

The study introduces a novel contour plot encoding method combined with neural networks for soybean maturity prediction from UAV imagery, improving accuracy and scalability over traditional methods.

Findings

Achieved up to 85% accuracy in maturity prediction.

Demonstrated robustness of the model with reduced temporal data.

Quantified the trade-off between temporal resolution and prediction accuracy.

Abstract

Plant breeding programs require assessments of days to maturity for accurate selection and placement of entries in appropriate tests. In the early stages of the breeding pipeline, soybean breeding programs assign relative maturity ratings to experimental varieties that indicate their suitable maturity zones. Traditionally, the estimation of maturity value for breeding varieties has involved breeders manually inspecting fields and assessing maturity value visually. This approach relies heavily on rater judgment, making it subjective and time-consuming. This study aimed to develop a machine-learning model for evaluating soybean maturity using UAV-based time-series imagery. Images were captured at three-day intervals, beginning as the earliest varieties started maturing and continuing until the last varieties fully matured. The data collected for this experiment consisted of 22,043 plots collected across three years (2021 to 2023) and represent relative maturity groups 1.6 - 3.9. We utilized contour plot images extracted from the time-series UAV RGB imagery as input for a neural network model. This contour plot approach encoded the temporal and spatial variation within each plot into a single image. A deep learning model was trained to utilize this contour plot to predict maturity ratings. This model significantly improves accuracy and robustness, achieving up to 85% accuracy. We also evaluate the model's accuracy as we reduce the number of time points, quantifying the trade-off between temporal resolution and maturity prediction. The predictive model offers a scalable, objective, and efficient means of assessing crop maturity, enabling phenomics and ML approaches to reduce the reliance on manual inspection and subjective assessment. This approach enables the automatic prediction of relative maturity ratings in a breeding program, saving time and resources.