This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations.

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas.

To address the problem of inefficient continuous multi-task scheduling operations, a scheduling model was established with the optimization objective of minimum total scheduling time to solve the problem of agricultural machinery continuous performing multiple tasks in agricultural production in hilly areas. An Improved Non-dominated Sorting Genetic Algorithm-III (Improved NSGA-III) was proposed to solve the agricultural machinery scheduling problem. The algorithm employed a two-stage coding mechanism to ensure the uniform distribution of the population and reduce the search solution space. The selection of paternal individuals is based on crowding degree, enabling individuals with better genes to participate in evolution. The domain search strategy of partial solution was introduced to improve the global search ability of the algorithm. The performance of the Improved NSGA-III algorithm and the other three algorithms was compared using farmland data from the experimental area. Results showed that the Improved NSGA-III algorithm outperforms the other three algorithms in terms of minimum total scheduling time, with reductions of 13.89%, 43.81% and 57.19%, respectively, verifying the effectiveness and reliability of the algorithm.

In response to the issue of harvesting machine failures affecting crop harvesting timing, this study develops an emergency scheduling model and proposes a hybrid optimization algorithm that combines a genetic algorithm and an ant colony algorithm. By enhancing the genetic algorithm’s crossover and mutation methods and incorporating the ant colony algorithm, the proposed algorithm can prevent local optima, thus minimizing disruptions to the overall scheduling plan. Field data from Deyang, Sichuan Province, were utilized, and simulations on various harvesting machines experiencing random faults were conducted. Results indicated that the improved genetic algorithm reduced the optimal comprehensive scheduling cost during random fault occurrences by 47.49%, 19.60%, and 32.45% compared to the basic genetic algorithm and by 34.70%, 14.80%, and 24.40% compared to the ant colony algorithm. The improved algorithm showcases robust global optimization capabilities, high stability, and rapid convergence, offering effective emergency scheduling solutions in case of harvesting machine failures. Furthermore, a visual management system for agricultural machinery scheduling was developed to provide software support for optimizing agricultural machinery scheduling.

With the advantages of high torque and low noise, traction drive continuously variable transmissions (TDCVTs) have a promising application in future vehicles. However, their efficiency is limited by spin losses caused by the different speed distributions between the contact areas of the traction. To overcome this shortcoming, this paper proposes a novel zero-spin design methodology applicable to any type of TDCVTs. The methodology analyzes the features of TDCVTs in terms of the variation of contact position and the shifting motion of traction components. It also establishes a mathematical model resulting in differential equations, whose general solution is the substitute for the equation of traction components generatrix. After applications of the methodology to two original TDCVTs, two zero-spin TDCVTs are proposed. A computational method of spin ratios, which are in direct proportion to spin losses, of four TDCVTs is introduced. The results of comparisons demonstrate that the proposed methodology can dramatically reduce the spin losses.

Against the background of comprehensively promoting the rural revitalization strategy, the replacement of agricultural labor by agricultural machinery in the hilly area of southwest China has become an indispensable input in the maize production process. Based on the national statistics from 2016 to 2022, the changes of maize planting area, yield, production costs and mechanization level in the southwest hilly area were analyzed through multiple regression. The grey correlation method was used to analyze the influence of production costs and mechanization level of maize planting on its economic efficiency, and the relationship between comprehensive mechanization rate and cost profit margin was predicted by the ridge regression model. The results show that (1) In addition to the planting area, the maize yield, production costs and mechanization level in the southwest hilly area showed an overall upward trend, among which the net profit was negative for six consecutive years, mainly because the labor costs accounted for too much of the total costs; (2) The average annual increase in the level of maize mechanization is 16%, but it is still lower than the national average; (3) Under the condition that other factors remain unchanged, for every 1% increase in the comprehensive mechanization rate, the cost profit margin increases by 0.467%, and it is determined that the most important factors affecting the growth of maize production economic efficiency are the sowing rate and the yield rate. It put forward suggestions to strengthen the mechanization of maize planting and develop maize agricultural harvesting machinery suitable for the hilly area.