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.
Abstract (translated)
植物育种项目需要评估成熟所需天数，以确保准确选择并将其安排在合适的测试中。在育种流程的早期阶段，大豆育种项目会给实验品种分配相对成熟的评级，以指示其适合的成熟区域。传统上，对育种品种成熟度值的估计涉及育种者手动检查田地，并通过视觉评估来确定成熟度值。这种方法高度依赖于评估者的判断，因此具有主观性和耗时性。本研究旨在开发一种基于无人机时间序列图像评估大豆成熟的机器学习模型。图像采集每隔三天进行一次，从最早开始成熟的品种开始，持续到最晚成熟的品种完全成熟为止。该实验收集的数据包括三年（2021年至2023年）内收集的22,043个地块，代表相对成熟组别1.6至3.9。我们使用从时间序列无人机RGB图像中提取的等高线图作为神经网络模型的输入。这种等高线图方法将每个地块内的时空变化编码为单张图像。训练了一个深度学习模型来利用此等高线图预测成熟度评级，该模型显著提高了准确性和鲁棒性，达到了最高85%的准确性。我们还评估了减少时间点数量时模型的准确性，量化了时间分辨率与成熟预测之间的权衡。这一预测模型提供了一种可扩展、客观且高效的作物成熟度评估手段，使表型学和机器学习方法能够减少对人工检查和主观评价的依赖。这种方法使得在育种项目中自动预测相对成熟评级成为可能，节省时间和资源。
URL
https://arxiv.org/abs/2412.09696
