With the gradual increase in the annual production of citrus, the efficiency of human labor has become the bottleneck limiting production. To achieve an unmanned citrus picking technology, the detection accuracy, prediction speed, and lightweight deployment of the model are important issues. Traditional object detection methods often fail to achieve balanced effects in all aspects. Therefore, an improved YOLOv7 network model is proposed, which introduces a small object detection layer, lightweight convolution, and a CBAM (Convolutional Block Attention Module) attention mechanism to achieve multi-scale feature extraction and fusion and reduce the number of parameters of the model. The performance of the model was tested on the test set of citrus fruit. The average accuracy (mAP_@0.5) reached 97.29%, the average prediction time was 69.38 ms, and the number of parameters and computation costs were reduced by 11.21 M and 28.71 G compared with the original YOLOv7. At the same time, the Citrus-YOLOv7 model’s results show that it performs better compared with the current state-of-the-art network models. Therefore, the proposed Citrus-YOLOv7 model can contribute to solving the problem of citrus detection.

Goat farming is one of the pillar industries for sustainable development of national economies in some countries and plays an active role in social and economic development. In order to realize the precision and intelligence of goat breeding, this paper describes an integrated goat detection and counting method based on deep learning. First, we constructed a new dataset of video images of goats for the object tracking task. Then, we took YOLOv5 as the baseline of the object detector and improved it using a series of advanced methods, including: using RandAugment to explore suitable data augmentation strategies in a real goat barn environment, using AF-FPN to improve the network’s ability to represent multi-scale objects, and using the Dynamic Head framework to unify the attention mechanism with the detector’s heads to improve its performance. The improved detector achieved 92.19% mAP, a significant improvement compared to the 84.26% mAP of the original YOLOv5. In addition, we also input the information obtained by the detector into DeepSORT for goat tracking and counting. The average overlap rate of our proposed method is 89.69%, which is significantly higher than the 82.78% of the original combination of YOLOv5 and DeepSORT. In order to avoid double counting as much as possible, goats were counted using the single-line counting based on the results of goat head tracking, which can support practical applications.

The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It is challenging to obtain a high-quality 3D crop surface feature composition for 3D reconstruction. In this paper, we propose a wheat point cloud generation and 3D reconstruction method based on SfM and MVS using sequential wheat crop images. Firstly, the camera intrinsics and camera extrinsics of wheat were estimated using a structure-from-motion system with feature maps, which effectively solved the problem of camera point location design. Secondly, we proposed the ReC-MVSNet, which integrates the heavy parametric structure into the point cloud 3D reconstruction network, overcoming the difficulty of capturing complex features via the traditional MVS model. Through experiments, it was shown that this research method achieves non-invasive reconstruction of the 3D phenotypic structure of realistic objects, the accuracy of the proposed model was improved by nearly 43.3%, and the overall value was improved by nearly 14.3%, which provided a new idea for the development of virtual 3D digitization.

The fruit of Xanthium strumarium L. (Asteraceae) has been used for the treatment of various inflammatory diseases. This study investigates the protective effect of caffeoylxanthiazonoside (CYXD) isolated from fruits of X. strumarium on sepsis mice in vitro and in vivo. Cecal ligation and puncture (CLP) operation was used to establish the sepsis mice model, and sham mice were also performed. CYXD was administered by intraperitoneal injection (10, 20, and 40 mg/kg/d), then the survival rate was measured in 96 h. Additionally, sepsis mice were induced by injection LPS (2 mg/kg); CYXD was administered by intraperitoneal injection (10, 20, and 40 mg/kg/d), then mice were sacrificed, and serum levels of TNF-α and IL-6 were determined by ELISA assay. Furthermore, the ability of CYXD to neutralize LPS was measured by using the LAL test, and expressions of TNF-α, IL-6 were determined by using real-time fluorogenic PCR. Results indicated that CYXD significantly elevated survival rates of sepsis mice induced by CLP (p < 0.05) with survival rates of 35%, 45%, and 65%. Furthermore, the LPS level was decreased obviously by CYXD (1, 2, and 4 mg/L) (p < 0.05). Additionally, CYXD (10, 20, and 40 mg/kg) can not only significantly decrease TNF-α and IL-6 levels induced by LPS in mice's serum (p < 0.05), but also inhibit mRNA expressions of TNF-α and IL-6 induced by LPS in RAW 264.7 cells at doses of 20, 40, and 80 μg/mL (p < 0.05). Our study demonstrated that CYXD has significant protective effects on sepsis mice.