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In response to the problems of low efficiency in traditional forestry surveying and insufficient real-time perception and navigation capabilities of intelligent agricultural machinery in complex forest environments, this study proposes a forestry crop pose estimation and intelligent agricultural machinery collaborative navigation method that integrates improved Random Sample Consensus (RANSAC) algorithm and lightweight visual detection. By improving the RANSAC algorithm, automated measurement of tree diameter at breast height and 3D pose estimation can be achieved, and high-precision offline point cloud maps can be constructed; On this basis, a lightweight online visual perception module is integrated to achieve real-time detection and localization of dynamic obstacles. A complete navigation system architecture has been developed, which deeply integrates offline 3D reconstruction and online real-time perception. In the field testing of intelligent agricultural machinery navigation, the fusion strategy reduces the maximum lateral tracking deviation from 0.85 m when relying only on the global path on challenging paths containing Unmapped obstacles to 0.28 m, effectively improving the navigation accuracy, obstacle avoidance ability, and operational safety of agricultural machinery in complex forest environments. This study provides a systematic solution for autonomous navigation of forestry intelligent agricultural machinery through the collaborative technology of offline 3D maps and online visual systems, which has strong practical application value.

Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities.

Smart agricultural machinery is built upon traditional agricultural equipment, further integrating modern information technologies to achieve automation, precision, and intelligence in agricultural production. Currently, significant progress has been made in the autonomous operation and monitoring technologies of smart agricultural machinery in China. However, challenges remain, including poor adaptability to complex environments, high equipment costs, and issues with system implementation and standardization integration. To help industry professionals quickly understand the current state and promote the rapid development of smart agricultural machinery, this paper provides an overview of the key technologies related to autonomous operation and monitoring in China’s smart agricultural equipment. These technologies include environmental perception, positioning and navigation, autonomous operation and path planning, agricultural machinery status monitoring and fault diagnosis, and field operation monitoring. Each of these key technologies is discussed in depth with examples and analyses. On this basis, the paper analyzes the main challenges faced by the development of autonomous operation and monitoring technologies in China’s smart agricultural machinery sector. Furthermore, it explores the future directions for the development of autonomous operation and monitoring technologies in smart agricultural machinery. This research is of great importance for promoting the transition of China’s agricultural production towards automation and intelligence, improving agricultural production efficiency, and reducing reliance on human labor.

In order to better promote the use of intelligent agricultural machinery, enhance the efficiency of grain production, optimize resource utilization, and effectively address the practical problem of the reduction in the rural labor force, while theoretically clarifying the mechanism that affects the adoption of intelligent agricultural machinery by farmers in Changsha County. Based on a questionnaire survey of farmers in Changsha County, Hunan Province, the ordered logit model was used to identify the significant factors influencing farmers’ adoption of intelligent agricultural machinery. The empirical results show that male farmers, farmers with a non-agricultural occupation, and farmers with a lower education level (below high school) have a lower willingness to adopt intelligent agricultural machinery. As the risk of purchasing intelligent agricultural machinery decreases, market demand increases, and the number of agricultural services provided by the government increases, the likelihood of farmers adopting intelligent agricultural machinery also increases. Based on these findings, this paper proposes targeted suggestions aimed at increasing the adoption of intelligent agricultural machinery by farmers in Changsha County, Hunan Province.

Object detection in unstructured agricultural environments remains challenging due to large scale variations, complex backgrounds, irregular obstacle shapes, and limited computational resources. To address these challenges, this paper proposes Agri-DETR, an efficient end-to-end detection framework based on the Real-Time Detection Transformer (RT-DETR), with coordinated improvements in feature perception, multi-scale representation, spatial reconstruction, and bounding box regression. Specifically, a lightweight backbone with a high-resolution feature branch is introduced to enhance the representation of small and fine-grained targets. A large selective feature fusion module is designed to strengthen multi-scale contextual modeling and improve feature discrimination under complex backgrounds. In addition, an attention-enhanced dynamic upsampling module refines high-resolution feature reconstruction, while a scale–shape–geometry-aware Intersection over Union (SSGIoU) loss improves localization stability for irregular and elongated objects. Experimental results show that Agri-DETR achieves 66.0% Average Precision (AP) on the self-constructed Agricultural Obstacle Dataset (AO-Dataset), outperforming representative detectors while reducing the parameter count by approximately 25% compared with RT-DETR-R18 baseline. In particular, small-object AP increases by 1.4%, demonstrating improved detection capability for small obstacles. Cross-dataset evaluation on COCO2017 further shows that Agri-DETR achieves 48.3% AP, demonstrating favorable generalization capability beyond the agricultural domain. These results indicate that Agri-DETR achieves an effective balance among detection accuracy, model complexity, and practical efficiency, making it a promising solution for real-world agricultural obstacle detection.

Today, China has become the world’s largest agricultural machinery and equipment countries and the use of countries, agricultural machinery industry for more than a decade to maintain high growth, along with three revolutionary changes in agricultural technology, has initially ushered in the development of artificial intelligence stage. At present, relative to the weak foundation of China’s agricultural machinery standardization work and the requirements of high integration of technology and multidisciplinary integration of artificial intelligence, there are bottlenecks that constrain the problem. This paper makes a directional discussion on the direction of standardization breakthrough through the analysis of digital design technology and typical applications at home and abroad, the analysis of standard transformation of scientific and technological achievements, and the analysis of current problems. Aiming at technical standards development, implementation, and improvement to promote technological innovation, application, and improvement of intelligent manufacturing of agricultural machinery products. So that scientific and technological innovation continues to enhance the level of technical standards, technical standards continue to promote the transformation of scientific and technological achievements. Technical standards and intelligent agricultural machinery lend each other in international competition and become a strategic means to participate in international cooperation and competition. Accelerate the transformation of the old and new dynamics of the agricultural mechanization industry, and realize the standardization and internationalization of the intelligent agricultural mechanization industry.

This paper proposes a path-tracking method for agricultural vehicles based on the 4WIS-4WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, α and R, on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation d, and heading deviation θ is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation d and heading deviation θ are the input variables. Values of α and R are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse.

The advancement of society and technology has promoted the development of modern agriculture. It has become a trend to replace traditional manpower with intelligent agricultural machinery that operates independently. As the core technology of intelligent agricultural machinery, complete coverage path planning technology has become more important. At present, the complete coverage path planning algorithms still suffer from problems such as sacrificing the coverage rate to obtain the minimum energy consumption, taking a long time to calculate the algorithm, and destroying crops across the covered region. In view of the above problems, an improved complete coverage path planning algorithms based on backtracking is proposed combined with the actual needs of intelligent agricultural machinery for planting seedlings to improve four aspects: repeated coverage, search efficiency, path planning, and sub-regional crossing. Firstly, the Morse decomposition method is used to divide a complex farmland region into simple sub-regions. Then an improved backtracking method based on a greedy algorithm is proposed in order to reduce the computational efficiency of the current region connection algorithms. The priority principle and the strategy of moving along the boundary are used to solve the problems of region crossing and sacrificing the coverage rate, thereby improving the performance of the current complete coverage path planning method. Compared with the traditional backtracking method, the experimental results show that the number of backtracking points is decreased by about 70% and the occurrence of crossing sub-regions has been significantly reduced. This proposed method can improve the coverage and operating efficiency of intelligent agricultural machinery operations and provide technical support for agricultural operations such as sowing, tillage, and harvesting, thus improving the quality and efficiency of agricultural production.

Path tracking is a crucial part of agricultural machinery automatic navigation system (ANS) and has been extensively investigated in prior research. Although existing ANS designs perform satisfactorily under mild soil condition, path-tracking algorithms are often challenged by unknown disturbances arising from complicated field environment and machine conditions. The current literature lacks a detailed analysis of the influence of the sideslip angle under specific operating speeds and path scenarios for agricultural machinery, which serves as the primary motivation for this study. In this paper, simulations are conducted for sprayers and harvesters across various paths, curvatures, and speeds to analyze the impact of sideslip on path-tracking performance. The results indicate that under the typical low-speed and large-curvature conditions of agricultural machinery, neglecting sideslip effects leads to a mismatch between the theoretical model and the actual vehicle motion. Compared to an MPC based on a kinematic model that disregards the sideslip angle, explicitly incorporating the sideslip angle into the kinematic model reduces the maximum lateral tracking error from 0.234 m to 0.174 m for a U-shaped path, and from 0.263 m to 0.194 m for a rectangular-shaped path. Simulation at different travel speeds further demonstrates that proposed algorithm achieves smaller sideslip amplitudes and faster attenuation after completing turns compared to conventional MPC. These findings offer valuable insights for the design of path-tracking algorithms in agricultural machinery autonomous driving systems.

Automatic navigation based on dual-antenna real-time kinematic (RTK) positioning has been widely employed for unmanned agricultural machinery, whereas GNSS inevitably suffers from signal blocking and electromagnetic interference. In order to improve the reliability of an RTK-based navigation system in a GNSS-challenged environment, an integrated navigation system is preferred for autonomous navigation, which increases the complexity and cost of the navigation system. The information fusion of integrated navigation has been dominated by Kalman filter (KF) for several decades, but the KF cannot assimilate the known knowledge of the navigation context efficiently. In this paper, the geometric characteristics of the straight path and path-tracking error were employed to formulate the constraint measurement model, which suppresses the position error in the case of RTK-degraded scenarios. The pseudo-measurements were then imported into the KF framework, and the smoothed navigation state was generated as a byproduct, which improves the reliability of the RTK positioning without external sensors. The experiment result of the mobile vehicle automatic navigation indicates that the tracking error-constrained KF (EC-KF) outperforms the trajectory-constrained KF (TC-KF) and KF when the RTK system outputs a float or single-point position (SPP) solution. In the case where the duration of the SPP solution was 20 s, the positioning errors of the EC-KF and TC-KF were reduced by 38.50% and 24.04%, respectively, compared with those of the KF.