The heat stress response of broilers will adversely affect the large-scale and welfare of the breeding of broilers. In order to detect the heat stress state of broilers in time, make reasonable adjustments, and reduce losses, this paper proposed an improved Cascade R-CNN (Region-based Convolutional Neural Networks) model based on visual technology to identify the behavior of yellow-feathered broilers. The improvement of the model solved the problem of the behavior recognition not being accurate enough when broilers were gathered. The influence of different iterations on the model recognition effect was compared, and the optimal model was selected. The final average accuracy reached 88.4%. The behavioral image data with temperature and humidity data were combined, and the heat stress evaluation model was optimized using the PLSR (partial least squares regression) method. The behavior recognition results and optimization equations were verified, and the test accuracy reached 85.8%. This proves the feasibility of the heat stress evaluation optimization equation, which can be used for reasonably regulating the broiler chamber.

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.

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.

Grain crops are regarded as fundamental to China’s agricultural production and food security. Effective control of nocturnal phototactic pests is essential for ensuring crop yields and achieving sustainable agricultural development. However, traditional solar insecticidal lamps often suffer from low energy utilization efficiency, dynamic switching control schemes, and poor adaptability in multi-pest coexistence scenarios. A multi-period intelligent switching control optimization scheme based on integrating a multi-pest phototactic rhythm is proposed, focusing on Cnaphalocrocis medinalis and Chilo suppressalis in rice fields. By considering the phototactic behavioral rhythm, energy consumption patterns, and residual energy levels, the proposed scheme dynamically optimizes the switching cycles of solar insecticidal lamps to maximize pest control effectiveness and energy efficiency. The rhythm modeling approach and dynamic adjustment mechanisms are employed to accurately align insecticidal working hours with varying pest activity patterns, thereby improving the pest control effectiveness of IoT-based solar insecticidal lamps. Simulation experiments demonstrate that, compared to traditional switching control schemes, the dynamic switching control scheme improves the average insecticidal rate by 17.7%, increases the effective insecticidal energy efficiency value by approximately 66.1%, and enhances the energy utilization rate by about 38.5%. The proposed dynamic switching control and intelligent energy management scheme not only improves the precision of pest control and energy utilization but also promotes the more efficient application of networked solar insecticidal lamps in smart agriculture. This work provides theoretical support and practical reference for intelligent pest control in complex agricultural environments, promoting the precision and sustainability of pest management practices.

Accurately counting the number of pests killed by solar insecticidal lamp (SIL) is crucial as it provides valuable insights into local pest density. However, a challenge arises when pests adhere to the high voltage metal mesh, causing continuous discharging and resulting in inaccurate counting using existing voltage counting methods. To address this issue, this research proposes a novel method that combines the counting of discharge sound and discharge voltage. The proposed method analyzes the discharge sound signal in the time domain, utilizing the time difference between pulses to filter out redundant data generated by discharges. The processed discharge sound data and discharge voltage data are jointly counted by machine learning, which significantly improves the accuracy and feasibility of counting. Comparative analysis shows that the accuracy of this method is 90.02%, which is 11.24% higher than that of voltage counting method (78.78%) and 34.63% higher than that of comparison method (55.39%). Finally, the accuracy of verification in real farmland environment is 93.28%. These results demonstrate the effectiveness of this paper method in achieving accurate pest counting. Furthermore, this paper method boasts lightweight implementation and easy deployment in consumer electronic devices such as SIL used for pest early warning.

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%.