Aiming at the problems of poor adaptability and low operational safety of agricultural machinery in hilly and mountainous areas, this study conducts the design and experimental research on a high-precision autonomous positioning system for the multi-crop combined harvester. The Beidou and Inertial Measurement Unit (IMU) high-precision positioning system based on Network Real-Time Kinematic (NRTK) was designed, and the hardware selection and software development of the autonomous positioning system were completed. The NRTK fixed base station in complex field environments was deployed Based on 4G communication, and dynamic differential calculations were performed with the random positions of the machinery to accurately obtain positioning data. Using the STM32L475 chip as the core information processor, efficient processing of real-time position information of combined harvester was realized based on coordinate conversion and attitude error correction of the autonomous positioning system. Performance tests were carried out using a self-developed the combined harvester. The results show that: the positioning error of the designed autonomous positioning system is less than 2 cm; when the harvester's speed ranges from 0.4 to 1.2 m/s, the maximum speed measurement error is less than 0.05 m/s, and the average speed measurement error is approximately 0.014 m/s, which meets the autonomous positioning accuracy requirements of the machinery.

As complex agricultural machinery, traditional combine harvesters face numerous challenges during operation due to their reliance on manual observation. To meet the demands of modern agriculture, intelligent combine harvesters have emerged. Intelligent sensing uses multi-sensor fusion and deep learning to monitor crop lodging, feed rate, loss rate, and impurity content. Under suboptimal conditions, multi-source fusion strategies improve perception reliability. Information processing and decision-making enable dynamic optimization of operational parameters and reduce harvest losses. Multi-machine coordination transforms single-machine operations into fleet control, while remote monitoring leverages a cloud edge collaboration architecture to enable status visualization, remote control, and predictive maintenance for faults. Unmanned operations utilize high-precision positioning and intelligent path planning to improve fleet efficiency and field coverage. However, the field still faces common challenges, including insufficient real-time processing capabilities for multi-source heterogeneous data, poor adaptability to complex agronomic scenarios, and limited economic feasibility. In this review, we examine the complete technology chain, which includes intelligent perception, intelligent decision-making and coordination, remote monitoring, and unmanned operations. We conduct a comparative analysis of the current state of these systems and the challenges they face, providing a systematic reference for future research and industrial applications.

Wheat crown rot is a destructive soil-borne disease affecting major wheat-growing regions worldwide. Mechanical transmission represents an important pathway for the spread of wheat diseases and combine harvesters, essential for large-scale farming operations, potentially facilitate long-distance pathogen dispersal through infected residue. However, the precise role of combine harvesters in the epidemiology of wheat crown rot remains unclear. The study revealed that the residue in the grain tank was significantly higher than that in other components, accounting for 44%-69% of the total residue. Pathogen isolation, identification, and PCR-based rapid detection confirmed the consistent presence ofFusarium pseudograminearumandF. graminearum in harvester residues following cross-regional operations from infected fields. Additionally, field trials demonstrated that after operating in disease-free fields, combine harvesters carrying pathogens led to disease incidence of 12% and 17% over two consecutive years. The application of physical and chemical treatments to harvesters prior to cross-regional operations significantly reduced field disease incidence. This study provides the first systematic evidence that combine harvesters serve as efficient vectors for long-distance dissemination of wheat crown rot pathogens, establishing a critical foundation for developing targeted biosecurity measures in wheat production systems.

In precision agriculture, the assessment and estimation of key crop parameters are crucial aspects for the optimisation of input usage and, as an ultimate goal, for the improvement of yield quality and quantity. In this context, a reliable prediction of yield by remotely sensed imagery is an enabling technology for optimisation. In this work, an innovative method for estimating yield in maize cultivation is presented, which exploits multi-temporal and multispectral Sentinel-2 satellite imagery with supervised Machine Learning (ML) techniques. For model training and validation, yield ground truth experimental data from combine harvesters was used, enabling the yield estimation at sub-field scale. The investigation, which was conducted on five case study plots, involved a preliminary comparison of four ML-based algorithms, trained with raw spectral bands. An assessment of the effect of the training dataset on the yield prediction accuracy was then performed. A set of Vegetation Indices (VIs) and Two Band Indices (TBIs) was also considered for this purpose. Finally, a multi-temporal analysis was conducted, in which the temporal evolution of crop spectral data over the maize growing season was exploited using imageries acquired in different epochs. The obtained results proved that an accurate estimation of maize yield can be reached using a Gaussian process regression model, exploiting multi-temporal features directly provided by the raw spectral bands. The model showed a high accuracy in the estimation of maize yield, even when fed with data acquired during only the maize vegetative phase, thus proving its capacity as a prediction tool. • Supervised machine learning techniques are used to estimate maize yield. • Combine harvester ground truth data enables prediction at subfield scale. • Multi-temporal imageries from Sentinel-2 improve the estimation. • Gaussian Process Regression algorithms reach accuracies up to R 2 > 0.9

Combine harvesters are core modern grain production equipment with high reliability, critical for food security. Yet their metal parts suffer severe grain-induced wear during operation, directly reducing efficiency, increasing grain loss, and raising maintenance costs and environmental burdens. This paper clarifies the grain-induced wear source characteristics and the dominant mechanisms and hazards for combine harvester metal surfaces, as well as summarizes the research progress of four key protection strategies: wear-resistant materials, surface engineering, structural and parameter optimization, and maintenance and remanufacturing. Based on the latest research data, the working principles, performance advantages and application scenarios of various protective technologies were analyzed. Current research faces several challenges: insufficient systematic wear data for multiple crops, unclear multi-factor coupled wear mechanisms, limited low-cost and long-lasting protective technologies, and the absence of online wear monitoring techniques. Finally, the directions for future research focus, such as the systematic research on the wear characteristics of multiple crops, the deepening of the wear mechanism of multi-factor coupling, the development of green, low-cost and long-term protection technologies, and the development of online wear monitoring and active control systems, are explored, providing theoretical support and technical reference for the transformation of wear control in combine harvesters, from passive maintenance to active protection throughout the entire life cycle. Such future work supports the high-quality development of agricultural mechanization and ensures food security.

Field breeding trial-plot harvesting is one of the key processes in crop breeding, as any mixing between varieties during harvest directly leads to the invalidation of breeding data. Therefore, achieving zero-residue self-cleaning inside the machine during harvesting is essential. Existing studies have largely relied on simulations to optimize cleaning parameters. However, research specifically targeting the synergistic design of the mechanical and pneumatic components of the cleaning device to achieve efficient and thorough self-cleaning in complex real-world conditions remains lacking. To address this issue, this paper presents a novel cleaning system specifically designed for efficient self-cleaning and optimizes its key parameters. Key structural parameters of the straw walker, vibrating sieve, and cleaning fan were analyzed, establishing preliminary ranges for crank speed, sieve-airflow angle, and fan speed. A test bench was developed, and single-factor experiments were conducted to investigate the effects of these parameters on core self-cleaning indicators, including the self-cleaning rate and self-cleaning time. The optimal parameter combination was obtained using the Box–Behnken design (BBD) response surface methodology: a crank speed of 390.80 r/min, a sieve-airflow angle of 29.88°, and a fan speed of 1995 r/min. Bench tests validated that the system achieved excellent cleaning performance while ensuring a self-cleaning rate of 100% and a reduced self-cleaning time of 20 s. The system’s effectiveness was further validated through field experiments using a 4LX1 prototype harvester on three wheat varieties. Results demonstrated zero grain mixing between plots, with self-cleaning times of 9–12 s. Both bench and field test results exceeded the relevant standards, effectively resolving the long-standing issue of grain residue in trial plot harvesting. Through dual validation, this study provides a referential solution for addressing grain residue and establishes a theoretical foundation for the synergistic design of efficient and precision breeding harvest technologies.

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Domestically developed wheat breeding plot combine harvesters in China currently utilize cyclone separation self-cleaning systems. However, these systems struggle to meet the agronomic requirement of zero wheat grain residue. Seed mixing caused by residual grains can compromise the accuracy of entire breeding field trials. This study focused on the structural design of a cyclone separation self-cleaning system based on high self-cleaning agronomic requirements. Research was conducted on the key structural and operational parameters of the cyclone separator and the negative-pressure centrifugal fan, preliminarily determining the ranges for critical parameters such as the diameter of the cylindrical section of the separator wall, the dust outlet diameter, and the rotational speed of the negative-pressure centrifugal fan. A test bench for the cyclone separation self-cleaning system of wheat breeding plot combine harvesters was designed and developed. Through single-factor experiments and Box–Behnken design optimization, the effects of key parameters on system performance were investigated. The optimal parameter combination—cylindrical section diameter of 614 mm, dust outlet diameter of 290 mm, and fan speed of 1495 r/min—achieved a self-cleaning rate of 100%, self-cleaning time ≤12 s, loss rate of 1.70%, and impurity rate of 0.16%, fully meeting the requirements for high-quality, rapid, and effective separation and self-cleaning operations.

To investigate the mechanical characteristics of the vibrational air-sucking sea buckthorn harvester, a system dynamics model integrating the harvester and the sea buckthorn tree was developed in this study.The model incorporates the adsorption force of the sucking device and the mechanical interactions between branches and fruits.Through numerical simulations, the influence of excitation frequency, lifting height, vibration isolation spring stiffness, crank radius, and adsorption force on the displacement of each component in the vibration system was systematically analyzed.Unlike vibration-only or suction-only prototypes, the proposed model simultaneously incorporates vibratory excitation and vacuum suction, which yields a 15 % higher detachment rate at 25 % lower trunk acceleration, thereby mitigating tree damage.The dynamic analysis demonstrates the vibration displacement of all components peaks at an excitation frequency of 10 rad/s.Increasing the stiffness of vibration isolation springs enhances the harvester's anti-vibration performance while amplifying the relative displacement between the tree trunk, branches, and fruits.Notably, the lifting height exhibits minimal impact on the harvest rate, whereas enlarging the crank radius significantly increases vibration displacement.Additionally, the application of adsorption force promotes fruit detachment by augmenting vibrational amplitude.This study provides theoretical insights for optimizing the design of vibration-adsorption combined harvesters.

Increasing food security today is not only a real action in agricultural activities, but has implemented information technology for data processing, and has been used in various fields, especially agriculture based on the internet and multimedia, especially millennial farmers in utilizing technology to increase their farming productivity. The study was conducted from February to April 2025 in Galung Village. The farmer population was 153 people, 15% were selected so that 23 people were selected as samples. The study aims to examine social media and supporting and inhibiting factors for the use of social media in Rice Farming in Galung Village. Data analysis used is descriptive and analysis using the Likert Scale. The results of the study showed that the use of social media Youtube and WhatsApp for hand tractors had a very influential value of 3.7 and 3.6, Transplanters via Youtube, Facebook and WhatsApp had an influence, quite influential with values 2.5, 1.5 and 1.8. Combine Harvester had an influence, quite influential, values 2.8, 2.4 and 1.3. Technology in the form of seeds, fertilizers and medicines had

Rice harvesting machine in Lampung Province has been commonly used for both personal use and contracting system. This opens up business opportunities for the provision of spare parts and machine repair services, especially during the main harvest season. Determining office locations or business policies in a region requires an analysis of the internal and external factors of the business itself. The analytical method used in this study was Spatial Data Analysis (SDA) to determine the types of strategies and policies that must be carried out by dealers of Kubota brand harvesting machines in Lampung Province. This decision support system is based on the results of spatial data analysis at the sub-district level. The results of spatial data analysis that combines data on paddy field area, slope level, and machine acceptance level show that there are six groups of potential priority areas included the sub-district recommendation for placing part shop and comparing with the existing active dealer part shop. There are six areas group, and dealer has cover 4 of them. Dealer is suggested to add two more-part shop that located in Suoh and Sungkai Utara to cover all areas group that can cover all area within 2 hours by motorbike. Keywords: Combine harvester, spatial analysis, location determination analysis, decision support system

To address the pronounced issue of noise and vibration within the combine harvester cab, this study proposes a hybrid simulation and experimental validation approach that integrates the pivot noise transfer function (NTF) with a finite element method (FEM)-based vibroacoustic coupling analysis. A coupled finite element model combining the cab structure and its internal acoustic cavity was developed, with the excitation path characteristics explicitly defined. The coupled interaction between structural and acoustic modes, along with its influence on noise transmission, was systematically examined. The analysis revealed a significant transmission peak near 18 Hz at critical pivot Point D under specific excitation directions, indicating strong directional sensitivity in the excitation–response relationship. Experimental validation showed that the discrepancy between simulated and measured responses, including the NTFs, remained within 15%, confirming the accuracy and applicability of the proposed method. This research offers a reliable analytical framework and practical reference for noise and vibration reduction in agricultural machinery cab design.

During field operations, combine harvesters frequently encounter significant fluctuations in feeding rates attributable to the stochastic nature of crop density and uneven growth patterns. These irregularities often precipitate critical mechanical failures, such as threshing drum blockages and engine stalling. To mitigate these operational risks, this study proposes a robust adaptive control strategy for feeding rate regulation rooted in fuzzy logic. Initially, a mathematical model of the system dynamics is constructed by analyzing the nonlinear coupling between the harvester’s forward speed and the feeding rate, incorporating characteristics of significant inertia and pure time delay. Addressing the inherent limitations of conventional PID algorithms—specifically their inadequate parameter adaptability and weak disturbance rejection under complex, time-varying conditions—a fuzzy adaptive PID controller is designed. This controller utilizes the feeding rate error () and its rate of change as inputs to facilitate online, real-time tuning of the proportional, integral , and derivative parameters via a fuzzy inference mechanism. Simulation experiments conducted on the MATLAB/Simulink platform demonstrate that, compared to traditional PID control, the proposed system reduces overshoot by approximately 14.2% (from 18.3% to 4.1%) when subjected to step changes in crop density. Furthermore, the settling time is significantly truncated, and steady-state error is effectively eliminated. These results corroborate that the proposed control strategy exhibits superior robustness and dynamic tracking capabilities, thereby satisfying the rigorous requirements for automated operation in modern precision agriculture.

Calculating Fuel Efficiency for Head-Feeding Combine Harvesters

A study to assess the role Custom Hiring Service Centres in the mechanized Paddy production was undertaken by gauging the knowledge and adoption levels of farmers of coastal taluks of Udupi district. The study revealed that the overall knowledge and adoption levels of farmers on farm implements and services available at Custom Hiring and Service Centres (CHCS) were at medium level with both Government sponsored and Private owned CHSCs users. The respondents were fully aware (100%) of tillage machineries including the tractors and tillers, advanced on-line booking facilities, availabilities of operators, technicians and experts, machinery rental charges, their repair & services, charges, mobile SMS facilities, provision of printed brochures about CHSCs. However, they were fully ignorant about the facilities of aggregate or model facility and machinery working duration tracking system APP in mobiles for computing rental charges. Demonstrations and trainings offered by the Government sponsored CHSCs helped their uses to acquire and adopt the technology of seedling preparations for machine transplanting (60%); but, private owned CHSC reliant farmers, depended fully upon the supplied seedlings. Only power tiller and cage wheels were fully used by all the respondents among the tillage machineries. M.B. Ploughs and Rotary tillers were not at all used by any of the respondents. Adoption level of Plant protection equipments was limited only for the Knapsack sprayers and Gutter sprayers. Similarly, the Cono weeders were partially adopted among the Government sponsored (20.00%) and Private owned (36.67%) CHSCs users. Neither, the power weeders nor paddy threshers were adopted by any of the respondents for weeding and threshing operations. Combine harvesters were adopted because of its versatility in harvesting, threshing and bagging advantages by all the respondents.

Crawler-type combine harvesters feature labor-intensive operation, tough steering and complex environments in paddy fields, necessitating reliable automatic operation to ensure efficient and complete harvesting. An error threshold-based autonomous navigation system for crawler-type combine harvesters was developed by using right-angle turning according to unilateral brake steering. Based on the chassis structure and working principles, a moving control system was designed to achieve automatic control of steering, speed and throttle. A global path planning method was proposed to generate a spiral path by giving reference points and operation directions. A path tracking method based on the error threshold was developed to calculate both lateral and heading errors in real-time, and we executed the adjustment strategy to ensure rapid alignment and high-precision tracking. A right-angle turning method was implemented to prevent missed cutting and crop damage by giving an adjustment distance. Field tests showed that the maximum lateral and heading errors for straight-line path tracking were 10.25 cm and 1.94°, respectively. The maximum lateral and heading errors for right-angle turning were 17.64 cm and −14.46°, respectively. It was concluded that the newly developed autonomous navigation system showed adequate path tracking accuracy and stability, meeting working requirements in crop harvesting.

ABSTRACT Despite extensive research on climate‐related vulnerabilities in Bangladesh's wetland ecosystems, there remains limited empirical evidence on the drivers of combined harvester adoption in rice cultivation, particularly in flash flood‐prone regions. This study examines the determinants of combined harvester adoption and its subsequent effects on market participation and household welfare among smallholder farmers in the wetland areas of Sunamganj district in Bangladesh. Leveraging a multistage random sampling technique, we collected household‐level data from 200 boro rice growers. The empirical strategy employs a Probit model to identify the determinants of combined harvester adoption and a Heckman two stage selection model to account for potential selection bias in estimating the effects on market participation and welfare outcomes. Our findings indicate that combined harvester adoption is significantly influenced by farm size, household income, land tenure arrangement, and access to agricultural information. Landowners exhibit an 11.4% higher likelihood of adoption, while access to information increases adoption probability by 16.1%, underscoring the salience of information asymmetries in technology diffusion. Moreover, combined harvester adoption significantly enhances market participation, leading to higher per capita income and expenditure among smallholders. Additionally, Heckman two stage estimates indicate that adopters benefit from increased productivity and reduced labor constraints than non‐adopters. These findings highlight the importance of improving information access, strengthening extension services, and enabling land consolidation to promote combined harvester adoption in the clmate vulnerable wetland areas. Overall, expanding information access, strengthening extension services, and supporting larger farm operations can significantly boost combined harvester adoption and market participation, ultimately improving smallholder resilience and livelihoods.

Precise distinguishing of maize blends and the evaluation of kernel losses enhances the accurate measurement of harvest loss. To address the low accuracy and poor anti-interference ability of traditional maize kernel detection methods under complex conditions, this paper proposes a multi-channel kernel impact detection algorithm based on discrete wavelet transform (DWT). The algorithm extracts feature band energies of kernel impacts through DWT multi-resolution analysis and counts kernels based on the duration of the energy signal. Therefore, weak signals are able to be effectively detected, thus correcting the missed errors that traditional monitoring systems produce for weak kernel signals. The monitoring system’s efficacy was assessed across various operational conditions. Test findings reveal that within the operating ranges of kernel flow rate of 20–40 kernels/s, sensor mounting angle of 30–60°, and mounting height of 300–500 mm, the system’s average detection accuracy reaches 94.4% and maintains good stability under different conditions. Compared with traditional detection systems, the system designed in this research exhibits superior sensitivity to weak kernel signals and higher monitoring accuracy. Finally, it was verified via practical field experiments that the designed sensor basically achieved the expected performance, and the recognition accuracy of the kernels in the mixture reaches 94%.

Excessive swing of the cantilevered conveyor trough is a key issue restricting the working efficiency and operational stability of combine harvesters. To suppress its swing, this study established a dynamic model of the conveyor trough to reveal the influence mechanisms of the initial angle, overall length, and cylinder pivot length on its swing characteristics. Orthogonal experimental design and multi-factor analysis of variance were employed to systematically analyze the significance of these three factors on swing amplitude, identifying cylinder pivot length as the most dominant factor. Optimization results determined the optimal parameter combination as an initial angle of 48.33°, an overall length of 1.45 m, and a cylinder pivot length of 1.1 m. Field tests verified that this optimized scheme reduces the swing amplitude by 11.62%, with a minimal error of 0.57% between theoretical and measured values, providing a reliable theoretical and experimental basis for the low-vibration design of combine harvester conveying mechanisms.

Current sensor-free transfer window alignment with combined energy harvesting and current measurement capabilities