Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.

Carbon dioxide (CO₂) enrichment in an agricultural environment has been shown to enhance the efficiency of crop photosynthesis, increasing crop yield and product quality. There is a problem of the excessive use of CO₂ gas when the CO₂ is enriched for crops, such as soybean and other field crops. Given the application of micro-nanobubbles (MNBs) in agricultural production, this research takes CO₂ as the gas source to prepare the micro-nanobubble water by the dissolved gas release method, and the response surface methodology is used to optimize the preparation process. The results show that the optimum parameters, which are the gas–liquid ratio, generator running time, and inlet water temperature for the preparation of CO₂ micro-nanobubble water, are 2.87%, 28.47 min, and 25.52 °C, respectively. The CO₂ content in the MNB water prepared under the optimum parameters is 7.64 mg/L, and the pH is 4.08. Furthermore, the particle size of the bubbles is mostly 255.5 nm. With the extension of the storage time, some of the bubbles polymerize and spill out, but there is still a certain amount of nanoscale bubbles during a certain period. This research provides a new idea for using MNB technology to increase the content and lifespan of CO₂ in water, which will slow the release and increase the utilization of CO₂ when using CO₂ enrichment in agriculture.

Video processing plays an important role in the intelligent monitoring and management system of agricultural information. Video shot boundary detection is the basic symmetry step underlying video processing techniques. According to the current shot boundary detection algorithm, the feature changes between gradual transition frames are difficult to detect, and the misdetection situation is caused by ignoring the attention of the target feature during the feature extraction. A novel symmetry multi-step comparative scheme of shot boundary detection algorithm based on global features and target features is proposed. First, the RGB color histogram features of the video frame are extracted. Second, foreground object detection for the video frames is performed using the Gaussian Mixture Model (GMM), and the scale-invariant features transformation (SIFT) of the foreground targets is extracted. Finally, global features and target features fusion through weights, calculating the difference between adjacent frames across multiple steps, generate a pattern distance map. The pattern distance map of the gradual transition and the cut detection is different; we can judge the gradual transition and the cut detection according to the pattern distance map. Experiments show that the proposed symmetry method improves by about 2% in recall and accuracy compared to other algorithms.