Monitoring growth behavior of maize plants such as the development of ears can give key insights into the plant's health and development. Traditionally, the measurement of the angle of ears is performed manually, which can be time-consuming and prone to human error. To address these challenges, this paper presents a computer vision-based system for detecting and tracking ears of corn in an image sequence. The proposed system could accurately detect, track, and predict the ear's orientation, which can be useful in monitoring their growth behavior. This can significantly save time compared to manual measurement and enables additional areas of ear orientation research and potential increase in efficiencies for maize production. Using an object detector with keypoint detection, the algorithm proposed could detect 90 percent of all ears. The cardinal estimation had a mean absolute error (MAE) of 18 degrees, compared to a mean 15 degree difference between two people measuring by hand. These results demonstrate the feasibility of using computer vision techniques for monitoring maize growth and can lead to further research in this area.

翻译：监测玉米植株的生长行为（如果穗的发育）可为植物健康状况与发育进程提供关键洞察。传统上，果穗角度的测量依赖人工完成，耗时耗力且易受人为误差影响。为应对这些挑战，本文提出一种基于计算机视觉的系统，用于在图像序列中检测与追踪玉米果穗。所提出的系统能够准确检测、追踪并预测果穗的朝向，有助于监测其生长行为。相较于人工测量，该方法可显著节省时间，并为果穗朝向研究开辟新途径，有望提升玉米生产效率。通过结合目标检测与关键点检测的算法，系统可检测出90%的果穗。其朝向估计的平均绝对误差（MAE）为18度，而人工双次测量的平均差异为15度。这些结果证明了利用计算机视觉技术监测玉米生长的可行性，并为该领域的后续研究奠定了基础。