In order to solve the problems of high difficulty and low efficiency of manual counting in scientific research of cotton aphid population prediction and control，a cotton aphid image recognition algorithm based on YOLO neural network was proposed and developed into software. First，the images of artificially inoculated cotton aphids were taken by mobile phone for 15 consecutive days，50 clear images were selected and cut into 6 sub-images，and then the training set and test set were obtained by using LabelImg software for manual labeling. Then，10 models from the series of YOLOv5 and YOLOv8，whose training parameters were set as the same (batch size was 32，iteration was 100 rounds，initial learning rate was 0.01，and periodic learning rate was 0.01)，were selected and trained by using the server of the AutoDL platform. Finally，the trained models were tested，and the YOLOv8l model showed the best overall performance，with mAP50 reaching 0.926. In order to provide users with convenient and easy-to-use man-machine software，the front end of the software was developed by using PYQT5，to realize the functions of reading and counting of cotton aphid pictures，visualizing results and exporting results to Excel. The back end of the software adopted an image processing method of "splitting-detection-merging"，which ensuring the efficient detection of YOLO model on small targets. After testing，the software had an average precision of 0.945 for counting dead cotton aphids and live cotton aphids，which was comparable to manual counting and had good practical value. This research may provide an intelligent detection tool for researchers related to cotton aphid control and also provided key operation information for precise operation in scenes such as unmanned farms.

Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.

Horticultural products such as fruits, vegetables, and tea offer a range of important nutrients such as protein, carbohydrates, vitamins and lipids. However, the present yield and quality do not meet the requirements of the rapid population growth associated with global climate change, the decline in horticultural practitioners, poor automation, and epidemic diseases such as COVID-19. In this context, smart horticulture is expected to greatly improve the land output rates, resource-use efficiency, and productivity, all of which should facilitate the sustainable development of the horticulture industry. Emerging technologies, such as artificial intelligence, big data, the Internet of Things, and cloud computing, play an important role. This paper reviews past developments and current challenges, offering future perspectives for horticultural chain management. We expect that the horticulture industry would benefit from integration with smart technologies. This requires the use of novel solutions to build a new advanced system encompassing smart breeding, smart cultivation, smart transportation, and smart sales. Finally, a new development approach combining precise perception, smart operation, and smart control should be instituted in the horticulture industry. Within 30 years, we expect that the industry will embrace mechanical, automatic, and informational production to transform into a smart industry.