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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]