Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R²), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R² values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.

The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately and quickly. To this end, a semantic segmentation model based on an improved U-Net is proposed in this paper to address the issue of efficient and accurate identification of vegetable crops and weeds. First, the simplified visual group geometry 16 (VGG16) network is used as the coding network of the improved model, and then, the input images are continuously and naturally down-sampled using the average pooling layer to create feature maps of various sizes, and these feature maps are laterally integrated from the network into the coding network of the improved model. Then, the number of convolutional layers of the decoding network of the model is cut and the efficient channel attention (ECA) is introduced before the feature fusion of the decoding network, so that the feature maps from the jump connection in the encoding network and the up-sampled feature maps in the decoding network pass through the ECA module together before feature fusion. Finally, the study uses the obtained Chinese cabbage and weed images as a dataset to compare the improved model with the original U-Net model and the current commonly used semantic segmentation models PSPNet and DeepLab V3+. The results show that the mean intersection over union and mean pixel accuracy of the improved model increased in comparison to the original U-Net model by 1.41 and 0.72 percentage points, respectively, to 88.96% and 93.05%, and the processing time of a single image increased by 9.36 percentage points to 64.85 ms. In addition, the improved model in this paper has a more accurate segmentation effect on weeds that are close to and overlap with crops compared to the other three comparison models, which is a necessary condition for accurate spraying and accurate weeding. As a result, the improved model in this paper can offer strong technical support for the development of intelligent spraying robots and intelligent weeding robots.

Conservation agriculture is an important measure promoting sustainable agriculture in northeast China. Coulters in the conservation agriculture system are used to cut the excessive residue in strips, loosen soils, and create good seedbeds. Information on the performance of coulters worked in a field with or without corn residue coverage is lacking in the literature. In this study, five coulters were tested in two field conditions at three working velocities to compare their performance. The five coulters were four types of fluted coulters (8 W, 13 W, 18 W, and 25 W) and one notched-flat coulter (NF); the two field conditions were whole residue plots (WR) and no residue plots(NR), and the three working velocities were 8 km/h (V1), 10 km/h (V2), and 12 km/h (V3). All of the tests were tested at a tillage depth of 80 mm. The results showed that the maximum furrow width (W_f), furrow disturbance area (A), and residue coverage change (C) were significantly affected by the working velocity and coulter type, while the cutting force (F) and skid rate (S) were significantly affected by the residue coverage, working velocity, and coulter type. The NF was found to have the smallest furrow profile, residue coverage change, and cutting force, as well as the largest skid rate. Among the fluted coulters, as the wavenumber rose, the cutting force, furrow width, and furrow disturbance area all gradually decreased, while the skid rate and residue coverage change were gradually enhanced. The straw residual intensified the cutting force and reduced the skid rate, which changed by 11.6% and 20.9%, respectively. As the working velocity rose from 8 km/h to 12 km/h, the furrow width, furrow disturbance area, residue coverage change, cutting force, and skid rate increased by 26.5%, 16.5%, 44.6%, 8.2%, and 22.7%, respectively. The results reveal that the flat coulter and large-wavenumber fluted coulters (18 W and 25 W) have less cutting force and are more beneficial for cutting straw residue in residue coverage fields, while the small-wavenumber fluted coulters (8 W and 13 W) are suitable for loosening soil and constructing seedbeds. The cutting force has significant effects on the performance of cutting straw residue, loosening soils, and creating seedbeds.

To achieve efficient and accurate detection of blueberry fruit ripeness, this study proposes a lightweight yet high-performance object detection model—CES-YOLO. Designed for real-world blueberry harvesting scenarios, the model addresses key challenges such as significant visual differences across ripeness stages, complex occlusions, and small object sizes. CES-YOLO introduces three core components: the C3K2-Ghost module for efficient feature extraction and model compression, the SEAM attention mechanism to enhance the focus on critical fruit regions, and the EMA Head for improved detection of small and densely packed targets. Experiments on a blueberry ripeness dataset demonstrated that CES-YOLO achieved 91.22% mAP50, 69.18% mAP95, 89.21% precision, and 85.23% recall, while maintaining a lightweight structure with only 2.1 M parameters and 5.0 GFLOPs, significantly outperforming mainstream lightweight detection models. Extensive ablation and comparative studies confirmed the effectiveness of each component in improving detection accuracy and reducing false positives and missed detections. This research offers an efficient and practical solution for automated recognition of fruit and vegetable maturity, supporting broader applications in smart agriculture, and provides theoretical and engineering insights for the future design of agricultural vision models. To further demonstrate its practical deployment capability, CES-YOLO was successfully deployed on the NVIDIA Jetson Orin Nano platform, where it maintained real-time detection performance, with low power consumption and high inference efficiency, validating its suitability for embedded edge computing scenarios in intelligent agriculture.

To improve the drying efficiency, drying uniformity and germination rate after hot air drying of Cyperus esculentus seeds, this paper proposed a drying method that combines tempering and hot air drying. The drying curve of Cyperus esculentus was obtained by experiment. The influence of the drying method on the drying quality of Cyperus esculentus seeds and the trend of drying quality with the tempering process parameters (the tempering temperature, the moisture content of Cyperus esculentus at the beginning of tempering and the tempering duration) were analyzed by single-factor experiments. The regression models were established by star point design response surface methodology, and the relationships between the tempering process parameters and seed production quality indexes were analyzed. The results showed that the optimal combination of tempering process parameters was a tempering temperature of 24 °C, a moisture content of Cyperus esculentus at the beginning of tempering of 23%, and a tempering duration of 3 h. Under this combination, the tempering process increased the drying uniformity, germination rate and drying efficiency of Cyperus esculentus seeds by 21.122%, 4.205% and 22.832%, respectively. The error between the verification test value and the software optimization parameter value was acceptable. The study showed that the optimized tempering drying process significantly improved the drying quality of Cyperus esculentus seed production, and the results could provide a theoretical basis for production practices.

During corn harvesting operations, machine–straw–soil contact often occurs, but there is a lack of research related to the role of straw–soil contact. Therefore, in this study, a composite contact model of corn straw‒soil particles was established based on the discrete element method (DEM). First, the discrete element Hertz‒Mindlin method with bonding particle contact was used to establish a numerical model of the double-bonded bimodal distribution of corn straw, and bonding particle models of the outer skin‒outer skin, inner pulp‒inner pulp, and outer skin‒inner pulp were developed. The nonhomogeneous and deformable material properties were accurately expressed. The straw compression test combined with simulation calibration was used to determine some of the bonding contact parameters by means of the PB (Plackett–Burman) test, the steepest ascent test, and the BB (Box–Behnken) test. Additionally, Additionally, the Hertz-Mindlin with JKR (Johnson-Kendall-Roberts) + bonding key model was used to establish the numerical model of the soil particles, which was used to describe the irregularity and adhesion properties of the soil particles. The geometric model of the soil particles was established using the multisphere filling method. Finally, a composite contact model of corn straw‒soil particles was established, the contact parameters between straw and soil were calibrated via collision tests, inclined tests and inclined rolling tests, and the established composite contact model was further verified through direct shear tests between straw and soil. A theoretical foundation for the optimal design of equipment linked to maize harvesting is provided by this work.