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The plant factory is a form of controlled environment agriculture (CEA) which is offers a promising solution to the problem of food security worldwide. Plant growth parameters need to be acquired for process control and yield estimation in plant factories. In this paper, we propose a fast and non-destructive framework for extracting growth parameters. Firstly, ToF camera (Microsoft Kinect V2) is used to obtain the point cloud from the top view, and then the lettuce point cloud is separated. According to the growth characteristics of lettuce, a geometric method is proposed to complete the incomplete lettuce point cloud. The treated point cloud has a high linear correlation with the actual plant height (R2 = 0.961), leaf area (R2 = 0.964), and fresh weight (R2 = 0.911) with a significant improvement compared to untreated point cloud. The result suggests our proposed point cloud completion method have has the potential to tackle the problem of obtaining the plant growth parameters from a single 3D view with occlusion.

Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experiment, and a dataset named PlantStereo was built for reconstruction. Taking data size, disparity accuracy, disparity density, and data type into consideration, PlantStereo outperforms other representative stereo matching datasets. Experimental results showed that, compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy. More specifically, for PSMNet, the EPE and bad−3 error decreased 0.30 pixels and 2.13%, respectively. For GwcNet, the EPE and bad−3 error decreased 0.08 pixels and 0.42%, respectively. In addition, the proposed workflow based on stereo matching can achieve competitive results compared with other depth perception methods, such as Time-of-Flight (ToF) and structured light, when considering depth error (2.5 mm at 0.7 m), real-time performance (50 fps at 1046 × 606), and cost. The proposed method can be adopted to build stereo matching datasets, and the workflow can be used for depth perception in plant phenotyping.

Packaging is one of the least automated steps among all the fruit postharvest processes, which is time-consuming and labor-intensive. Therefore, a robust suction strategy for robotic manipulation needs to be developed. In this research, a geometric-based apple suction strategy for robotic packaging was studied, including suction cup design, optimal suction region selection algorithm, and robot system integration. In the first place, on the basis of the geometric features of the spheroid fruit, the structure of the suction cups was designed to provide reliable suction force. Then, suction force measurement experiments on both acrylic balls and apples were conducted. Based on the results, the parameters of the suction cup were finally determined. The results also indicated that the curvature radius of the suction region is supposed to larger than that of the suction cups. Furthermore, a robust suction region selection algorithm was developed, which involves four steps: RGB-D information acquisition, object detection and point cloud generation, spherical fitting, and suction region selection. Finally, the above methods were integrated into a robotic packaging system. In addition, on the basis of spatial-frequency domain imaging (SFDI) technology, early stage bruise was detected for validation. The results showed that, the proposed suction strategy and system is potential for robust robotic apple packaging. [ABSTRACT FROM AUTHOR]