Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

Seeds are the most basic and important means of production for agriculture. During the production and processing of seeds, they may undergo potential mechanical damages and mildew alterations, which might jeopardize their germination viability. Hence, checking the quality of seeds before sowing is of paramount importance for the benefit of the sower and the safety of agricultural production. In order to achieve an efficient detection of maize seed quality, our experiment assembled a dataset composed of 2,128 seeds with four different health statuses of maize: healthy, broken, moth-eaten, and mildewed. In this thesis, we proposed a lightweight maize seed quality detection model for small objects based on improved YOLOv8: I-YOLOv8. Firstly, we introduced a multi-scale attention mechanism called EMA to efficiently retain information across channels and reduce computational load. Next, we chosen the SPD-Conv module for low-resolution images and small objects, and applied it to the backbone, which addressed the loss of fine-grained information and the less efficient learning of feature representations present in YOLOv8. Lastly, we reduced the large detection layer, which directed the network to pay more attention to the location, channel, and dimensional information of smaller objects, and we also replaced the loss function with WIoUv3. We validated our model using ablation studies and compared it with YOLOv5, YOLOv6, and YOLOv8. The mAP (Mean Average Precision) of the improved model I_YOLOv8 reaches 98.5%, which is 6.7% higher than YOLOv8. The average recognition time per image was 163.9fps, a boost of 5.2fps compared to YOLOv8. This study lays a theoretical foundation for the efficient, convenient, and rapid detection of maize quality, while also offering a technical basis for advancing automated maize quality detection means.

Object detection in deep learning provides a viable solution for detecting crop-pest-infected regions. However, existing rectangle-based object detection methods are insufficient to accurately detect the shape of pest-infected regions. In addition, the method based on instance segmentation has a weak ability to detect the pest-infected regions at the edge of the leaves, resulting in unsatisfactory detection results. To solve these problems, we constructed a new polygon annotation dataset called PolyCorn, designed specifically for detecting corn leaf pest-infected regions. This was made to address the scarcity of polygon object detection datasets. Building upon this, we proposed a novel object detection model named Poly-YOLOv8, which can accurately and efficiently detect corn leaf pest-infected regions. Furthermore, we designed a loss calculation algorithm that is insensitive to ordering, thereby enhancing the robustness of the model. Simultaneously, we introduced a loss scaling factor based on the perimeter of the polygon, improving the detection ability for small objects. We constructed comparative experiments, and the results demonstrate that Poly-YOLOv8 outperformed other models in detecting irregularly shaped pest-infected regions, achieving 67.26% in mean average precision under $0.5$ threshold ($\mathrm{mA}{\mathrm{P}}_{50}$) and 128.5 in frames per second (FPS).

Traditionally, the supplement of organic manure in tea plantations has been a common approach to improving soil fertility and promoting terroir compounds, as manifested by the coordinated increase in yield and quality for the resulting teas. However, information regarding the effect of organic manure in the metabolome of tea plants is still inadequate. The metabolite profiles of tea shoots applied with cow manure, urea or no fertilizer were studied using gas chromatography–mass spectrometry (GC–MS). In total, 73 metabolites were detected, and the modulated metabolites included mainly amino acids, organic acids and fatty acids. In particular, glutamine, quinic acid and proline accumulated more in tea shoots in soils treated with cow manure, but octadecanoic acid, hexadecanoic acid and eicosanoic acid were drastically reduced. Pearson correlation analysis indicated that organic acids and amino acids in tea shoots were the two major metabolite groups among the three treatments. The analysis of metabolic pathways demonstrated that the cow manure treatment significantly changed the enrichment of pathways related to amino acids, sugars and fatty acids. Sensory evaluation showed that the quality of green teas was higher when the plants used to make the tea were grown in soil treated with cow manure rather than urea during spring and late summer. The results indicated that the application of cow manure in soils changed the metabolic characteristics of tea shoots and improved the qualities of the resulting teas.

Energy efficient utilization is an important criteria and factor that affects the design of wireless sensor networks (WSNs). In this literature, we propose an energy distance aware clustering protocol with Dual Cluster Heads using Niching Particle Swarm Optimization (DCH-NPSO). The protocol selects two cluster heads in each cluster, the Master Cluster Head (MCH) and the Slave Cluster Head (SCH), and the selection needs to consider the network state information carefully and deliberately. Simulation results show that the protocol we proposed can balance the energy dissipation and extend the network lifetime effectively.