To improve the detachability performance and remanufacturing capability of existing agricultural micro-tillers, a modular design concept is introduced into the product design and development process, with Axiomatic Design (AD) and the Design Structure Matrix (DSM) serving as the methodological guidance and theoretical framework. In the design process, “Z-mapping” is used to reconstruct the demand/function/structure of the agricultural micro-tiller, decompose the total functions step by step, and establish a design matrix to transform the initial user requirements into specific functional indicators and design parameters. Geometric correlation is used as the design evaluation index to assign values to the matrix to establish a DSM for the correlations between agricultural micro-tiller design parameters. Using system clustering to optimize the distribution of matrix values, a total of five functional modules were identified to achieve a modular design scheme and design a prototype for agricultural micro-tillers. This design solution has significantly better disassembly performance than existing products, effectively enhancing the remanufacturing capability of existing equipment, proving the effectiveness of the Kano-AD-DSM-based design strategy, and providing a new theoretical reference for the innovative design of other small agricultural equipment.

To improve the planning efficiency of the Hybrid A-Star algorithm in facility-based agriculture environments, we propose a novel method that integrates directional guidance and segment-based heuristic weighting. Jump point search (JPS) is first employed to generate a reference path, from which key jump points and their heading angles are extracted to guide node expansion. The path is then segmented into straight and turning segments, each assigned dynamic heuristic weights to form a segment-wise heuristic sequence. Simulation results show that, while maintaining comparable path lengths, the proposed method significantly improves computational efficiency, reduces node expansion, and enhances overall path smoothness. These results highlight the proposed method’s effectiveness for path planning in autonomous mobile platforms operating within agricultural facilities.

Regarding the path planning problem for the local leveling operation of the land leveler, this paper proposes a path planning method based on the improved dung beetle optimizer (IDBO) algorithm. Firstly, a comprehensive evaluation objective function was established for the local operation path planning of the land leveler, which included the path length, under-excavation amount, under-filling amount, as well as the total amount of excavated and filled soil. Then, IDBO algorithm was constructed, an initialization population strategy based on Fuch chaotic mapping and reverse learning strategy was designed, as well as an improved ball-rolling behavior that integrates the search strategy of the Aquila high soar with the vertical stoop from the Aquila optimizer algorithm. Test functions were used to verify the superiority of the IDBO algorithm compared to the dung beetle optimizer (DBO) algorithm, the particle swarm optimization (PSO) algorithm and the gray wolf optimizer (GWO) algorithm. Finally, taking the paddy fields in a real environment as the object, a hardware platform for data acquisition was constructed, and data collection, analysis, terrain modeling, and path planning experiments were carried out with paddy fields in the natural environment as the measured objects. The experimental results show that, for the primary optimization objective of load variation cost, as well as path length cost, compared with the other three algorithms (PSO, GWO, DBO), the IDBO algorithm achieved improvements of 7.0%, 12.3%, and 6.6% on Plot 1, 1.6%, 9.2%, and 1.6% on Plot 2, 4.0%, 6.1%, and 1.5% on Plot 3, and 3.3%, 24.1%, and 3.4% on Plot 4.

With limited coaching resources, automated training devices offer opportunities for self-directed sports practice. However, their practical value depends on users’ continued use. To identify the key determinants of continuance intention toward basketball shooting training machines, this study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task–Technology Fit (TTF) into an analytical framework. A mixed-method design was adopted, including prototype experience, interviews, and questionnaire surveys. A total of 429 valid questionnaires were collected from basketball enthusiasts recruited from universities, fitness centers, and public basketball courts. The results indicate that performance expectancy, task–technology fit, and effort expectancy all positively influence continuance intention. Among these factors, performance expectancy shows the strongest direct effect (β = 0.44, p < 0.001). In addition, task–technology fit reinforces both performance expectancy and effort expectancy. To translate these findings into design practice, the study further integrates the Function Analysis System Technique (FAST) and the Function–Behavior–Structure (FBS) framework, generating a design pathway from behavioral mechanisms to functional elements and structural implementation. These findings provide theoretical and practical support for the design of automated training devices.