In Northeast China, the maize (Zea mays L.) harvester header height is still manually controlled, and the control precision is poor, which limits the quality of maize harvesting and is unfavorable to the long-term development of agricultural harvesting. This work created an adaptive adjustment method for header height to address this issue. A maize harvester header, an STM32 control unit and key module, a hydraulic adjustment mechanism, and a pressure-wheel profiling device comprise the majority of the system. In this scenario, the proposed pressure-wheel profiling device is mounted to the ridge’s surface and walks along it, delivering real-time data on terrain changes. The terrain change signals are received and processed in real time by the STM32 control unit, which subsequently operates the hydraulic adjustment mechanism to alter the header height. The structural dimensions and operational parameters of the essential components for pressure-wheel profiling device were determined using force analysis. A kinematic analysis of the hydraulic adjusting mechanism was performed, and the expression of the relationship between the displacement of hydraulic cylinder and the header height was obtained. The pressure-wheel profiling device was calibrated, the adaptive adjustment parameter model was constructed, and the PID control technique was employed to achieve automatic header height adjustment. This study analyzed the effects of harvester different forward speeds (1.25, 1.45, 1.65, 1.85, and 2.05 m/s) and different operating modes (open and unopened system) on the test indexes (Errors in the automatic adjustment of header height, Cob loss rate, Coefficient of variation in stubble height). The results revealed that, with the adaptive adjustment system mode on, the average difference between the measured actual header height and the predetermined height was 9.96 mm, the average coefficient of variation in stubble height was lowered by 34.44%, and the average cob loss rate was decreased by 7.98%, both of which may accommodate the needs of maize harvester header height adjustment. This study serves as a reference for the automated design of a maize harvester for monopoly crops.

Robotic tomato picking is a crucial step toward mechanized and precision farming. Effective tomato recognition and localization algorithms for these robots require high accuracy and real-time performance in complex field environments. This study modifies the SSD model to develop a fast and high-precision tomato detection method. The classical SSD model is optimized by discarding certain feature maps for larger objects and incorporating a self-attention mechanism. Experiments utilized images from an organic tomato farm. The model was trained and evaluated based on detection accuracy, recall rate, time consumption, and model size. Results indicate that the modified SSD model has a 95% detection accuracy and 96.1% recall rate, outperforming the classical and self-attention SSD models in accuracy, time consumption, and model size. Field experiments also demonstrate its robustness under different illumination conditions. In conclusion, this study promotes the development of tomato-picking robots by presenting an optimized detection method that effectively balances accuracy and efficiency. This method improves detection accuracy remarkably. It also reduces complexity, making it very suitable for real-world use. It plays a crucial role in facilitating the adoption of robotic harvesting systems in modern agriculture. Technologically, it remarkably boosts the picking efficiency, lessens the reliance on human labor, and cuts down fruit losses through precise picking. As a result, it effectively enhances resource utilization efficiency, providing a practical solution for the development of sustainable agriculture.

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture.

Sustainable agriculture has garnered increasing attention in recent times, with corn stalk retention constituting a pivotal component of sustainable agricultural practices. Presently, whole corn stalk retention (CCR), three-year rotation corn stalk retention (TYR), and standing corn stalk retention (SCR) are prevalent corn stalk management techniques in northeast China. However, the question of which corn stalk management technique is best suited to specific local climates within northeast China remained unanswered. Therefore, this manuscript investigates the impact of these corn stalk management practices on nitrogen (N), phosphorus (P), potassium (K), and soil organic amendments by analyzing long-term practical data. To gather data for analysis, three locations with varying latitudes were selected. The results indicate that local climate has a significant influence (p < 0.05) on the decomposition process and level of retained corn stalks. In locations with sufficient annual accumulated temperature and precipitation, a larger amount of corn stalk retention is acceptable. For instance, CCR is deemed suitable for Liaoning Province in China. Conversely, in locations lacking sufficient annual accumulated temperature or precipitation, an excessive amount of corn stalk retention cannot decay completely within a given period. Consequently, farmlands cannot adsorb adequate soil nutrients or organic matter derived from decomposed corn stalks. Thus, TYR or SCR is more appropriate for Heilongjiang Province in China. The findings of this research can guide farmers in optimizing corn stalk management practices according to specific local climates.

Farmland soil respiration (Rs) significantly impacts the global carbon (C) cycle. Although nitrogen (N) can promote crop growth and increase yields, its relationship with Rs and its constituents, including autotrophic respiration (Ra) and heterotrophic respiration (Rh), remains unclear. Therefore, a field study was carried out in a cabbage (Brassica pekinensis Rupr) system to probe the impact of N addition on Rs, Ra, and Rh. Five levels of N addition, including 0 kg N hm⁻²·yr⁻¹ (N0), 50 kg N hm⁻²·yr⁻¹ (N50), 100 kg N hm⁻²·yr⁻¹ (N100), 150 kg N hm⁻²·yr⁻¹ (N150), and 200 kg N hm⁻²·yr⁻¹ (N200), started in March 2022. The Rs (Ra and Rh) and soil samples were measured and collected twice a month. The findings revealed the following: (1) N fertilizer enhanced Ra while reducing Rs and Rh; (2) soil temperature (ST), belowground net primary productivity (BNPP), soil inorganic N (SIN), and soil total C/total N (C/N) were the significant elements influencing Ra, and microbial biomass carbon (MBC), SIN, and microbial diversity (MD) were the primary factors influencing Rh; (3) partial least squares-path models (PLS-PM) showed that ST and SIN directly impacted Rh, while ST and BNPP tangentially influenced Ra; (4) 150 kg N hm⁻²·yr⁻¹ was the ideal N addition rate for the cabbage in the region. In summary, the reactions of Ra and Rh to N fertilizer in the Northeast Plains are distinct. To comprehend the underlying processes of Rs, Ra, and Rh, further long-term trials involving various amounts of N addition are required, particularly concerning worsening N deposition.

Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management.