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.