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The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines. [ABSTRACT FROM AUTHOR]

• The direct-seeding path for rice covers the entire field. • Rice direct-seeding path includes the encircling path around the field blocks. • Encircling path planning algorithm is applicable to different shapes fields. • The path planning encompasses various patterns and path sequences. Path planning is one of the key technologies that determines the efficiency and quality of field operations using autonomous agricultural machinery. To date, there has been extensive research on global coverage path planning for unmanned agricultural machinery within the working area; however, the issue of "encircling and edging" left by the machinery during headland turns is often overlooked. This study focuses on the operational conditions of unmanned agricultural machinery in the rice fields of southern China. The path planning problem is abstracted under physical constraints, such as the space at the field headland, the agricultural environment (e.g., types of field ridges), the turning characteristics of the machinery, turning radius, working width, and minimum safety distance, into mathematical constraints. This yields an optimized path planning model and sequence combination. Based on the improved Reeds–Shepp curve, an en-circling and edging path planning algorithm is designed. The planned path for the unmanned agricultural machinery from start to finish consists only of arcs and straight lines, suitable for the right-angle turning, reversing, and 180° U-turn operations required for rice field machinery, thus significantly improving the coverage of field edges and missed areas at the headland. A rice direct-seeding machine was used as a test subject for encircling and edging field trials, with tests conducted in rectangular, trapezoidal, and irregular quadrilateral rice fields. The results show that, for rectangular plots, the full-field coverage rate of rice direct-seeding operations can reach 96.44%; for trapezoidal plots, the coverage rate is 95.47%; and for irregular quadrilateral fields, the coverage rate is 95.69%. These results are promising, indicating that the encircling and edging path planning based on the improved Reeds–Shepp curve can effectively increase the full-field coverage rate of unmanned agricultural machinery, improving work quality and meeting the needs of modernized, intelligent agricultural machinery operations. [ABSTRACT FROM AUTHOR]