The efficient identification of rice pests and diseases is crucial for preventing crop damage. To address the limitations of traditional manual detection methods and machine learning-based approaches, a new rice pest and disease recognition model based on an improved YOLOv7 algorithm has been developed. The model utilizes the lightweight network MobileNetV3 for feature extraction, reducing parameterization, and incorporates the coordinate attention mechanism (CA) and the SIoU loss function for enhanced accuracy. The model has been tested on a dataset of 3773 rice pest and disease images, achieving an accuracy of 92.3% and an mAP@.5 of 93.7%. The proposed MobileNet-CA-YOLO model is a high-performance and lightweight solution for rice pest and disease detection, providing accurate and timely results for farmers and researchers.

Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform–near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky–Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with R_p = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with R_p = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with R_p = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with R_p = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves.

Pest infestations in rice crops greatly affect yield and quality, making early detection essential. As most rice pests affect leaves and rhizomes, visual inspection of rice for pests is becoming increasingly important. In precision agriculture, fast and accurate automatic pest identification is essential. To tackle this issue, multiple models utilizing computer vision and deep learning have been applied. Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. The paper enhances the original model by adding the self-developed RepPConv-block to reduce the problem of information redundancy in feature extraction in the model backbone and to a certain extent reduce the model parameters. The original model’s CCFM structure is enhanced by integrating the Gold-YOLO neck, improving its ability to fuse multi-scale features. Additionally, the MPDIoU-based loss function enhances the model’s detection performance. Using the self-constructed high-quality rice pest dataset, the model achieves higher identification accuracy while reducing the number of parameters. The proposed RP18-DETR and RP34-DETR models reduce parameters by 16.5% and 25.8%, respectively, compared to the original RT18-DETR and RT34-DETR models. With a threshold of 0.5, the average accuracy calculated is 1.2% higher for RP18-DETR than for RT18-DETR.

The traditional detection method of CO₂ concentration in seed respiration has defects such as low detection accuracy, low detection efficiency, and inability to monitor in real time. In order to solve these problems, we report a seed respiration CO₂ detection system based on wavelength modulation spectroscopy (WMS) techniques in tunable diode laser absorption spectroscopy (TDLAS). This system uses a 2004nm distributed feedback (DFB) laser as the light source, and a double-layer seed respiration device (about 1.5L) is designed based on Herriott cell with an effective optical path of about 21meters. Then, the second harmonic (2f) signal is extracted by the wavelength modulation method for CO₂ concentration inversion. When the ambient temperature and pressure changes greatly, the corrected 2f signal is used for CO₂ concentration inversion to improve the accuracy. A series of verification and comparison experiments have proved that the seed respiration CO₂ detection system has the advantages of strong stability, high sampling frequency, and high detection accuracy. Finally, we used the developed system to measure the respiration intensity and respiration rate of 1g corn seeds. The respiration intensity curves and respiration rate change details show that the seed respiration CO₂ detection system is more suitable for a small amount of seeds than nondispersive infrared (NDIR) CO₂ sensor and gas chromatography in real-time monitoring of the breathing process.

The analysis of greenhouse gas emission characteristics on urban roads is of great significance for understanding the sources and sinks of urban greenhouse gases and their changing patterns. Based on tunable diode laser derivative absorption spectroscopy technology, which features high resolution, high sensitivity, and fast response, a vehicle-mounted system capable of simultaneously detecting CO₂ and CH₄ has been developed. The system has a response time of 0.38 s, with detection sensitivities of 5 ppb for CH₄ and 0.2 ppm for CO₂, power consumption of approximately 4.8 W, a weight of less than 3 kg, and dimensions of 255 mm × 275 mm × 85 mm. Using this system, monitoring campaigns were conducted on the same road in Beijing, running north–south, during different time periods in April and June 2023. The results show that there is little correlation between changes in CO₂ and CH₄ concentrations on the road, and these gas concentrations exhibit different influencing factors and spatiotemporal characteristics. The CO₂ concentration on the road is primarily related to the degree of traffic congestion and does not exhibit significant seasonal variations. The average CO₂ concentration measured on the road is much higher than the global average CO₂ concentration during the same period. On the other hand, the CH₄ concentration on the road is not strongly correlated with traffic congestion but is closely related to the leakage of methane from specific emission wells or covers. The CH₄ concentration is higher in the morning, gradually decreases as the sun rises, and then increases again after sunset. The CH₄ concentration measured at night in June is significantly lower than that in April, reflecting some seasonal variation. The CH₄ concentration on the Beijing urban road is slightly higher than the global average CH₄ concentration during the same period. The vehicle-mounted experiments verified the feasibility of using this self-developed system for vehicle-mounted detection of greenhouse gas concentrations on urban roads. The research results can provide data for analyzing the spatial pattern of regional carbon sources and sinks.