Plant growing detection dataset

In this research, we present a technique aimed at identifying the evolving sections of plants utilizing RGB-D data, with the aim of automating the detection of plant growth within an extraterrestrial experimental setting. As humanity entertains the prospect of inhabiting space in the future, the cultivation of plants in outer space becomes imperative for sustaining food supplies. However, the feasibility of growing plants in space akin to terrestrial methods remains uncertain, necessitating exploration through cultivation experiments conducted aboard international space stations and similar platforms. The observation of plant growth in space is constrained by human resources and available measurement space, further compounded by the exorbitant transportation costs, which escalate with weight. Consequently, there is a preference for lightweight equipment. Traditional automatic plant growth measurement techniques often rely on bulky equipment or require a significant amount of measurement space, rendering them impractical for space applications. In this investigation, we propose a methodology for identifying growing plant sections employing just one RGB-D camera. This approach enables the construction of a measurement system utilizing only a single camera and a laptop for image storage and connection, thereby ensuring lightweight portability. Moreover, the fixed positioning of the camera for plant capture minimizes spatial requirements and reduces the need for manpower. Our proposed technique entails leaf segmentation through depth data and the detection of growing sections via local feature matching. Experimental trials using a model plant corroborated the effectiveness of our method in leaf segmentation and growing part detection. Additionally, the experimental outcomes showcased the capability of the proposed approach in pinpointing the growing sections by refining the matching areas based on segmentation outcomes and appropriate observation intervals.本研究提出一种基于RGB-D（红绿蓝深度）数据识别植物生长区域的技术，旨在实现地外实验环境下植物生长检测的自动化。随着人类畅想未来太空居住的愿景，在外太空种植植物对于保障粮食供应变得至关重要。然而，采用类似地面种植的方式在太空培育植物的可行性尚未明确，因此需要在国际空间站及类似平台上开展种植实验进行探索。太空环境下的植物生长观测受限于人力资源与可用测量空间，再加上随重量递增的高额运输成本，进一步加剧了观测难度，因此轻量化设备成为优选。传统的植物生长自动测量技术往往依赖笨重设备或需要大量测量空间，难以适配太空应用场景。本研究提出一种仅使用单台RGB-D相机即可识别植物生长区域的方法，该方案仅需单台相机与一台用于图像存储及连接的笔记本电脑即可搭建测量系统，确保了系统的轻量化与便携性。此外，固定安装的相机用于拍摄植物，可最大限度降低空间需求并减少人力投入。本研究提出的技术通过深度数据实现叶片分割，并通过局部特征匹配完成生长区域的检测。以模式植物开展的实验验证了所提方法在叶片分割与生长区域检测中的有效性。此外，实验结果表明，通过基于分割结果与合理观测间隔优化匹配区域，所提方法能够精准定位植物生长区域。