As technology progresses, smart automated systems will serve an increasingly important role in the agricultural industry. Current existing vision systems for yield estimation face difficulties in occlusion and scalability as they utilize a camera system that is large and expensive, which are unsuitable for orchard environments. To overcome these problems, this paper presents a size measurement method combining a machine learning model and depth images captured from three low cost RGBD cameras to detect and measure the height and width of tomatoes. The performance of the presented system is evaluated on a lab environment with real tomato fruits and fake leaves to simulate occlusion in the real farm environment. To improve accuracy by addressing fruit occlusion, our three-camera system was able to achieve a height measurement accuracy of 0.9114 and a width accuracy of 0.9443.

翻译：随着技术的进步，智能自动化系统将在农业领域扮演越来越重要的角色。现有的产量估算视觉系统因采用体积庞大且昂贵的相机系统，在遮挡和可扩展性方面面临挑战，不适用于果园环境。为解决这些问题，本文提出了一种结合机器学习模型与三个低成本RGBD相机深度图像的尺寸测量方法，用于检测并测量番茄的高度和宽度。该系统在实验室环境下使用真实番茄果实和假叶子评估其性能，以模拟真实农场环境中的遮挡情况。通过解决果实遮挡问题以提高精度，我们的三相机系统实现了高度测量精度0.9114和宽度精度0.9443。