This paper aimed to predict the mechanical composition of machine-picked fresh tea leaves (MPFTLs) using near-infrared spectroscopy (NIRS) rapidly and non-destructively. Samples of MPFTL with different mechanical composition ratios were collected and subjected to NIRS analysis. Subsequently, various preprocessing methods were employed to eliminate extraneous noise information. Next, characteristic spectral information was extracted using the backward interval partial least squares (biPLS) method, which was subsequently subjected to principal component analysis (PCA). Finally, a predictive model was constructed by applying the back propagation artificial neural network (BP-ANN) method, which was tested by external samples to assess its predictive efficacy, and the results were expressed as root mean square error and determination coefficient of prediction (R_p²). The optimal spectral pretreatment method was the following: (standard normal variate (SNV) + second derivative (SD)). Four characteristic spectral subintervals of ([2, 3, 7, 10]) were screened out, and the cumulative contribution rate of 95.20%, attributable to the first three principal components, was determined. When the tanh transfer function was applied to construct the BP-ANN-NIRS model, the results demonstrated optimal performance, exhibiting a root mean square error and a determination coefficient of prediction (R_p²) of 0.976 and 0.027, respectively. The absolute values of prediction deviation for all prediction set samples were found to be less than 0.04. The results of the best BP-ANN model for external samples were found to be in close agreement with those of the prediction set model. NIRS technology has successfully achieved the forecasting of the mechanical composition of machine-picked fresh tea leaves rapidly and accurately, providing a fair and convenient new method for purchasing fresh tea raw materials by machines, according to their quality, and promoting the sustainable high-quality and healthy development of the tea industry.

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions.

Scheduling agricultural machinery across multiple cooperatives is often inefficient because existing rigid, discrete assignment models fail to flexibly coordinate shared resources under tight time windows. To address this limitation, we develop a simulation-based framework for the Multi-cooperative Agricultural Machinery Scheduling Problem (MAMSP) underpinned by a Continuous Collaborative Workload Sharing (CWS) formulation. To mitigate the solution fragmentation inherent in continuous optimization, we propose a Hybrid Particle Swarm Optimization with Sparsity Repair (HPSO-SR). The algorithm integrates a stochastic initialization strategy to enhance global exploration, a mutation injection mechanism to avoid swarm stagnation, and a sparsity repair operator that prunes uneconomical fractional assignments, yielding operationally feasible sparse schedules. A real-world case study from Liyang, China, augmented by synthetic instances of varying scales (small, medium, and large), was conducted to benchmark the proposed approach against a rule-based heuristic, a Genetic Algorithm (GA-CWS), and Simulated Annealing (SA-CWS) under a unified decoding scheme. The results show that HPSO-SR consistently achieves the lowest objective values, reducing the total cost by 74.43% relative to GA-CWS and 59.20% relative to SA-CWS in the medium-scale case. By deliberately trading off minimal additional transfer cost against improved timeliness, the obtained schedules nearly eliminate delay penalties. Sensitivity analysis and mechanism ablation studies further confirm that the sparse solutions exhibit structural resilience and that the proposed repair strategy is essential for algorithmic convergence, supporting the reliability of the proposed approach for time-critical, high-stakes agricultural operations.

Chinese tea has a long history, and the development level of tea mechanization in this country is in the leading position internationally. Equipment for the tea processing craft is one of the most widely used types in China; indispensable technology is used for high-quality tea in the relevant tea processing equipment. The performance of tea processing equipment is always a focus when it is being used. Meanwhile, research evaluating the performance of tea processing equipment has been rarely reported. Thus, it is important to find a methodology to evaluate the performance of equipment for the tea processing craft. In this paper, a new method of rough set theory, as well as a radar chart for quality evaluation of tea processed by different tea processing crafts, is created. Firstly, the important indexes of processed tea are presented, including moisture, tea polyphenols, free amino acids, caffeine, and vitamin C. Then, these indexes for nine different types of processed tea based on different tea processing crafts are experimented and detected. Thirdly, rough set theory and radar chart are adopted to solving the weight of each index and establishing comprehensive evaluation diagram of all kinds of samples. Then, the merit of samples can be achieved. Meanwhile, the best tea processing equipment craft can be also obtained through comparisons between the sensory evaluation and the radar chart. It is concluded that sample 2, sample 3 and sample 4 belong to the best processing samples, and it is verified that the far-infrared radiation tea green removing machine has a significant influence on tea processing equipment craft. In addition, from the comprehensive analysis of green tea sample, not only tea quality but equipment craft of tea processing can be analyzed.

Coordinating a shared fleet across multiple owners under tight time windows is a challenging multi-objective problem balancing cost, timeliness, and equity. We study it for multi-cooperative agricultural machinery dispatch, formulating the Multi-Cooperative Agricultural Machinery Scheduling Problem under Continuous Workload Sharing (MAMSP-CWS) as a three-objective model that minimizes inter-area transfer cost, time-window violation, and cross-cooperative workload imbalance. To approximate the Pareto front, we develop a Multi-Objective Hybrid Particle Swarm Optimization with Tabu Search and Sparsity Repair (MO-HPSO-TS-SR), which couples particle-swarm search, tabu-search refinement, and a sparsity-repair operator within an external crowding-distance archive. The method is evaluated on three scales (a real instance from Liyang, China, and two synthetic ones) against NSGA-II-CWS and HTSMOGA-CWS over 20 independent runs each. MO-HPSO-TS-SR attains the best mean value on every metric-by-scale combination, with a decisive convergence advantage (hypervolume and IGD; Holm-adjusted $p<0.001$, Cliff’s $\delta \approx 1$). A mechanism decomposition identifies Sparsity Repair as the dominant contributor to hypervolume, with Tabu Search as a complementary refiner. The advantage over NSGA-II-CWS widens with problem scale, from 19.4% on the Small instance to 74.2% on the Large instance, reflecting the disproportionate degradation of the genetic baseline rather than a growing advantage of the proposed method. Beyond agriculture, the framework extends to other continuous-encoding scheduling problems, providing a transferable decision-support tool.