This paper presents a dataset with 5513 images of individual soybean seeds, which encompass five categories: (Ⅰ) Intact, (Ⅱ) Immature, (Ⅲ) Skin-damaged, (Ⅳ) Spotted, and (Ⅴ) Broken. Furthermore, there are over 1000 images of soybean seeds in each category. Those images of individual soybeans were classified into five categories based on the Standard of Soybean Classification (GB1352-2009) [1]. The soybean images with the seeds in physical touch were captured by an industrial camera. Subsequently, individual soybean images (227×227 pixels) were divided from the soybean images (3072×2048 pixels) using an image-processing algorithm with a segmentation accuracy of over 98%. The dataset can serve to study the classification or quality assessment of soybean seeds.

Aiming at the matter of seed overlapping and adhesion within the actual quality detection of soybean, a soybean image segmentation based on multi-scale Retinex with color restoration (MSRCR) was proposed. Firstly, original images of soybean were obtained by the industrial camera. secondly, these images were enhanced by MSRCR. Thirdly, these images after MSRCR were segmented by Otsu. Finally, soybean seed images were extracted by mask and minimum bounding rectangle from soybean images after MSRCR. The image acquisition system was constructed and the algorithm was verified. The experiment showed that the segmentation accuracy of the proposed algorithm was 98.05%, which can meet the requirement of soybean image segmentation. Additionally, the proposed algorithm can also provide an image preprocessing method for agricultural seed classification in similar scenes.

The Solar Insecticidal Lamp (SIL) is an electronic device designed for physical pest control, widely utilized in orchards and farmland. Currently, the characteristic of the phototactic rhythm of pest is commonly ignored in the design of SILs, hindering pest control. This phenomenon is particularly evident in the prolonged turning on/off lamp, which leads to inefficient energy utilization due to the lack of adjustment for peak pest activity. To address this issue, four models based on the phototactic rhythm of pests are developed to adjust the insecticidal timing of SIL for precise pest control. These mathematical models are established considering the phototactic rhythm of four pests that exert the most significant impact on crops, namely Mythimna seperata, Helicoverpa armigera, Proxenus lepigone, and Cnaphalocrocis medinalis. The results indicate that mathematical modeling of the phototactic rhythm of the pest is valuable in capturing their nocturnal activity patterns. The proposed mathematical model can help to optimize the on/off time of SIL for pest control. The integration of electronic devices such as SIL in pest management represents a noteworthy advancement in agricultural electronics, contributing to the progress of smart and sustainable agriculture.

The solar insecticidal lamp (SIL) is an innovative green control device. Nevertheless, a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency. The substantial energy consumption required to turn on the SIL, coupled with the extension of insecticidal working time during the low pest activity periods, can result in low energy efficiency. Especially when the energy storage level is below 50%, the inefficient use of energy significantly reduces the effectiveness of pest control. Consequently, an ineffective on/off scheme for these lamps may lead to suboptimal energy utilization. In this paper, we present the solar insecticidal lamp intelligent energy management scheme (SIL-IEMS) to address the challenge of inefficient energy utilization in the solar insecticidal lamp internet of things (SIL-IoT). SIL-IEMS primarily utilizes genetic algorithm (GA) and greedy algorithms to optimize insecticidal working time by considering constraints such as residual energy and the number of trap pests. Comparing SIL-IEMS to the traditional remote switching method (TRSM) and the solar insecticidal lamp genetic algorithm (SILGA), our simulation results showcase its superior energy efficiency and pest control effectiveness. Particularly noteworthy is the SILIEMS's 17.6% increase in insecticidal efficiency compared to TRSM and 6% improvement over SILGA when the SIL begins with a remaining energy level of 15%.

The camera is a core device for modern surveillance and data collection, widely used in various fields including security, transportation, and healthcare. However, their widespread deployment has proportionally escalated associated security risks. This paper initially examines the current state of research on attack methods targeting camera systems, providing a comprehensive review of various attack techniques and their security implications. Subsequently, we focus on a specific attack method against universal serial bus (USB) cameras, known as electromagnetic pulse (EMP) attacks, which utilize EMP to prevent the system from detecting the cameras. We simulated EMP attacks using a solar insecticidal lamp (which generates EMP by releasing high-voltage pulses) and a commercially available EMP generator. The performance of the cameras under various conditions was evaluated by adjusting the number of filtering magnetic rings on the USB cable and the distance between the camera and the interference source. The results demonstrate that some USB cameras are vulnerable to EMP attacks. Although EMP attacks might not invariably cause image distortion or permanent damage, their covert nature can lead to false detection of system failures, data security, and system maintenance. Based on these findings, it is recommended to determine the optimal number of shielding rings for cameras or their safe distance from EMP sources through the experimental approach outlined in this study, thereby enhancing the security and resilience of USB camera enabled systems in specific scenarios.

Currently, devices such as solar insecticidal lamps are widely used in agricultural pest control, but routine trapping is insufficient to meet the demands of precision agriculture. Therefore, determining the nocturnal phototactic rhythm of pests to optimize the control strategies of insecticidal lamps has become key to achieving precise pest control. However, existing automated monitoring and forecasting devices struggle to effectively monitor the nocturnal phototactic rhythm of small pests. To address this issue, this study developed an automated monitoring system for phototactic rhythm based on sticky traps and machine vision. For the hardware, an image acquisition device integrating a darkroom and scheduled supplementary lighting was designed to obtain stable time-series images of nocturnal pests. For the algorithm, the YOLO-STP detection model was proposed by improving upon the baseline YOLOv11 model. This model introduces a P2 detection layer, a Coordinate Attention (CA) mechanism, and a hybrid bounding box regression loss function integrating WIoU and NWD. Combined with a sliding window cropping method, it further enhances the detection capability for small objects. Additionally, an incremental counting method based on spatial cascade matching was proposed to mitigate counting errors caused by target occlusion or detachment in the time-series images. Experimental results indicate that the mean average precision (mAP) of the detection model was 93.2%. For the counting method, the coefficient of determination ($R^2$) was 0.98, with an RMSE of 1.97 and an MAE of 1.60. Field validation in real-world paddy fields demonstrated that the system can accurately record the abundance changes of 12 pest species, intuitively visualizing the differences in phototactic rhythms among various species. This study provides a viable automated monitoring tool for acquiring the nocturnal activity rhythm data of agricultural pests in the field.