Land preparation is an important component of fragrant rice production. However, the effect of tillage on fragrant rice production is unclear, especially regarding the biosynthesis of 2-acetyl-1-pyrroline (2-AP), which is the main compound of the unique aroma of fragrant rice. This study aimed to explore 2-AP biosynthesis in fragrant rice under different tillage regimes. Three tillage methods were applied in the present study: conventional rotary tillage (CK) as the control, plough tillage (PT), and no-tillage (NT). Compared with CK, the PT treatment increased 2-AP content in grain, upregulated the activity of ornithine aminotransferase (OAT) and increased contents of 1-pyrroline and pyrroline-5-carboxylic acid (P5C). Furthermore, the PT treatment increased the grain yield and nitrogen accumulation of fragrant rice. Meanwhile, the 2-AP content in the grain produced under the NT treatment was significantly higher than that in the grain produced under both the PT and CK treatments due to the enhancement of proline content and the activities of proline dehydrogenase (PDH) and △1-pyrroline-5-carboxylic acid synthetase (P5CS). However, the present study observed that the overall production of fragrant rice under NT conditions was inferior due to lower yield, nitrogen accumulation, and anti-oxidative enzymatic activities. Moreover, the organic matter content and soil microorganism quantity increased due to PT and NT treatments. Compared to CK, PT improved fragrant rice grain yield and nitrogen accumulation and induced an increase in OAT activity and led to an increase in 2-AP concentration. No-tillage also produced increased 2-AP content in grain by enhancing PDH and P5CS activities but limited yields and nitrogen accumulation in fragrant rice.

Agricultural and Biological Engineering

Accurate rice row detection is critical for autonomous agricultural machinery navigation in complex paddy environments. Existing methods struggle with terrain unevenness, water reflections, and weed interference. This study aimed to develop a robust rice row detection method by integrating multi-sensor data and leveraging robot travelling prior information. A 3D point cloud acquisition system combining 2D LiDAR, AHRS, and RTK-GNSS was designed. A variable-threshold segmentation method, dynamically adjusted based on real-time posture perception, was proposed to handle terrain variations. Additionally, a clustering algorithm incorporating rice row spacing and robot path constraints was developed to filter noise and classify seedlings. Experiments in dryland with simulated seedlings and real paddy fields demonstrated high accuracy: maximum absolute errors of 59.41 mm (dryland) and 69.36 mm (paddy), with standard deviations of 14.79 mm and 19.18 mm, respectively. The method achieved a 0.6489° mean angular error, outperforming existing algorithms. The fusion of posture-aware thresholding and path-based clustering effectively addresses the challenges in complex rice fields. This work enhances the automation of field management, offering a reliable solution for precision agriculture in unstructured environments. Its technical framework can be adapted to other row crop systems, promoting sustainable mechanization in global rice production.

To address the problems of incomplete coverage of complex plots and low efficiency in unmanned agricultural machinery operations, the study proposes the Human–Machine Collaboration (HMC). Targeting different types of plots, the study designed the method of area division based on the Breseham algorithm and the polygonal clipping algorithm. In addition, the study proposed a secondary division method of the area based on alternating point judgment and risk area evaluation function to ensure the security of the HMC. The experimental results show that the coverage rate of HMC is 100% and the field operation work efficiency is higher than 86% under different shapes of plots (rectangle, right trapezoid and ordinary quadrilateral). In the three shapes of plots, the operation scores of the HMC in the open edge area are 96.08, 163.39, and 137.4, respectively; the operation scores in other areas are 104.73, 89.88, 97.77, respectively; and the comprehensive scores are 162.36, 204.33, and 189.85, respectively, which are higher than those of unmanned operation and manned operation, showing comparatively better performance. The area division under the HMC meets the operational requirements, and the research provides technical support for unmanned farm development.