In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

Lettuce is an important vegetable in horticulture, but information about the interactive effects of light and temperature on its photosynthetic characteristics was inadequate. This work investigated the effects of three temperatures (15/10 (T15), 23/18 (T23), and 30/23 °C (T30)) and five light treatments (100 (P100), 200 (P200), 350 (P350), 500 (P500), and 600 μmol·m⁻²·s⁻¹ (P600)) on the light–response curves, chlorophyll content, and yield of lettuce. The results showed that the maximum photosynthetic rate, light saturation point, chlorophyll content, and yield of lettuce were all the highest at T23 compared with T15 and T30 under different light intensities. Under the same temperature conditions, the photosynthesis capacity and yield of lettuce in the P350 and P500 treatments at T15, P350, P500, and P600 treatments at T23 and P500, and P600 treatments at T30 were larger than other light treatments. The results suggested that temperature play had a more pronounced influence on photosynthesis and yield in lettuce, but the appropriate levels of light intensity improved its potential photosynthetic capacity and yield under different temperature conditions.

Phycocyanin, which covalently binds phycocyanobilin chromophores, is not only a candidate fluorescent probe for biological imaging, but also a potential antioxidative agent for healthcare. Herein, a plasmid harboring two cassettes was constructed, with cpcB from Spirulina subsalsa in one cassette and the fusion gene cpcS::ho1::pcyA in the other, and then expressed in Escherichia coli. PCB-CpcB(C-82), a fluorescent phycocyanin β subunit, was biosynthesized in E. coli, exhibiting an absorption maximum at 620 nm and fluorescence emission maximum at 640 nm. When cpcS was replaced by cpcT, PCB-CpcB(C-153), another fluorescent phycocyanin β subunit, was produced, exhibiting an absorption maximum at 590 nm and fluorescence emission maximum at 620 nm. These two fluorescent biliproteins showed stronger scavenging activity toward hydroxyl and DPPH free radicals than apo-CpcB. The IC₅₀ values for hydroxyl radical scavenging by PCB-CpcB(C-82), PCB-CpcB(C-153), and apo-CpcB were 38.72 ± 2.48 µg/mL, 51.06 ± 6.74 µg/mL, and 81.82 ± 0.67 µg/mL, respectively, and the values for DPPH radical scavenging were 201.00 ± 5.86 µg/mL, 240.34 ± 4.03 µg/mL, and 352.93 ± 26.30 µg/mL, respectively. The comparative antioxidant capacities of the proteins were PCB-CpcB(C-82) > PCB-CpcB(C-153) > apo-CpcB, due to bilin binding. The two fluorescent biliproteins exhibited a significant effect on relieving the growth of E. coli cells injured by H₂O₂. The results of this study suggest that the fluorescent phycocyanin β subunits of S. subsalsa were reconstructed by one expression vector in E. coli, and could be developed as potential antioxidants.

The process of agricultural robot design is a complex system requiring the cooperation and integration of agricultural, machinery, automation, and information technology. These demands create great challenges for the innovative design of agricultural robots. Meanwhile, more than 95% of the latest inventions and creations in the world are recorded in the patent literature. In order to make effective use of the information and data resources of patents, shorten the design cycle, and provide knowledge for the designers, according to the operation’s objectives, an agricultural robot technology knowledge graph (TKG) was established for innovative designs. By analyzing the patent information, a patent IPC co-classification network (IPCNet) for adaptive design process recognition was put forward to meet the requirements of the different operation objectives and operation links. Through the extraction of the technology keywords and efficacy keywords, based on the word co-occurrence network (WCONet), a technology–efficacy map (TEM) was constructed. Through the integration of the adaptive design process and the TEM, the agricultural robot design TKG was constructed for determining technological recommendations for agricultural robot design. The case of the citrus picking robot design was realized to implement the design process. With the technology recommendation results, the moving system, body, and end-effector for the citrus picking robot were designed to verify the results of the recommendation.