In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model’s receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value.

Precise weed recognition is an important step towards achieving intelligent agriculture. In this paper, a novel weed recognition model, Cotton Weed-YOLO, is proposed to improve the accuracy and efficiency of weed detection. CW-YOLO is based on YOLOv8 and introduces a dual-branch structure combining a Vision Transformer and a Convolutional Neural Network to address the problems of the small receptive field of the CNN and the high computational complexity of the transformer. The Receptive Field Enhancement (RFE) module is proposed to enable the feature pyramid network to adapt to the feature information of different receptive fields. A Scale-Invariant Shared Convolutional Detection (SSCD) head is proposed to fully utilize the advantages of shared convolution and significantly reduce the number of parameters in the detection head. The experimental results show that the CW-YOLO model outperforms existing methods in terms of detection accuracy and speed. Compared with the original YOLOv8n, the detection accuracy, mAP value, and recall rate are improved by 1.45, 0.7, and 0.6%, respectively, the floating-point numbers are reduced by 2.5 G, and the number of parameters is reduced by 1.52 × 10⁶ times. The proposed CW-YOLO model provides powerful technical support for smart agriculture and is expected to promote the development of agricultural production in the direction of intelligence and precision.

In this study, tunable diode laser absorption spectroscopy (TDLAS) combined with wavelength modulation spectroscopy (WMS) was used to develop a trace C₂H₂ sensor based on the principle of gas absorption spectroscopy. The core of this sensor is an interband cascade laser that releases wavelength locks to the best absorption line of C₂H₂ at 3305 cm⁻¹ (3026 nm) using a driving current and a working temperature control. As the detected result was influenced by 1/f noise caused by the laser or external environmental factors, the TDLAS-WMS technology was used to suppress the 1/f noise effectively, to obtain a better minimum detection limit (MDL) performance. The experimental results using C₂H₂ gas with five different concentrations show a good linear relationship between the peak value of the second harmonic signal and the gas concentration, with a linearity of 0.9987 and detection accuracy of 0.4%. In total, 1 ppmv of C₂H₂ gas sample was used for a 2 h observation experiment. The data show that the MDL is low as 1 ppbv at an integration time of 63 s. In addition, the sensor can be realized by changing the wavelength of the laser to detect a variety of gases, which shows the flexibility and practicability of the proposed sensor.