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  • Editorial
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16 November 2025

Innovative Design and Application of Modern Agricultural Machinery Systems in Cropping Systems

and
1
Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu, Pingtung 91201, Taiwan
2
Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2084, Australia
*
Authors to whom correspondence should be addressed.
Agriculture2025, 15(22), 2371;https://doi.org/10.3390/agriculture15222371 
(registering DOI)
This article belongs to the Special Issue Innovative Design and Application of Modern Agricultural Machinery Systems in Cropping Systems
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The recent increase in extreme weather events and the variety of crop types and planting patterns have shifted agricultural machinery research toward achieving consistent, high-quality performance in the field []. The emphasis is now on integrating systems as a whole rather than making small adjustments to individual mechanisms []. This includes determining machine size, configuring specialized components, implementing closed-loop control systems, employing sensor-driven decision making, and rigorously assessing field work quality [,,,]. Standardized testing procedures are increasingly employed to allow for valid comparisons to be made across different sites and to assess the performance of complete functional modules under realistic conditions.
Intelligent field operation platforms are usually designed to fit row spacing and terrain, and then mapped to an accessible workspace, with their mechanical strength verified through structural stress analysis []. In pineapple cultivation, a height-adjustable platform employs a gantry chassis with hydraulic elevation to regulate ground clearance. Finite element analysis identifies stress concentrations at beam-to-plate connections, informing targeted reinforcement and mass reduction. Field trial results demonstrate stable operation on twenty-degree slopes and plug-and-play integration with multiple task modules []. In litchi and longan orchards, a mechanized platform is designed based on tree and site characteristics, taking into account row spacing, plant spacing, tree height, canopy width, and local slope. This enables multi-step tasks to be performed and allows for the operating parameters of the platform to be quickly adjusted across different cultivation systems and terrains [,]. In complex farmland, autonomous navigation for tracked-chassis platforms has advanced to a model-assisted and learning-based approach, allowing for perception-driven decisions to be made under challenging field conditions [,]. In agricultural field work, accurately applying fertilizer and planting seeds improves efficiency and reduces input usage when important factors are identified and tracked. These factors include the depth of seeding, individual seed placement, consistency of material flow, and spacing between rows []. Utilizing sensors and smart control systems can enhance machinery efficiency and reduce costs. This approach makes it easier to reliably and quantifiably link crop growth with planting results []. In peanut farming, a dynamic depth system uses surface pressure sensors to assess slope and reconstruct bed geometry in real time. A dual-loop fuzzy controller with velocity feedforward can adjust for variations in the beds and changes in travel speed, ensuring a consistent planting depth during field trials []. Similarly, field assessments of pre-cut sugarcane planters show that the efficiency of these machines is influenced by the length of the segments, the width of the outlet, the sprocket speed of the metering system, and the travel speed [,].
To address the problems of poor conveying stability and inadequate air–fertilizer mixing uniformity in traditional feed machines, new designs now employ guide chamber blades that enhance both the uniformity of air–fertilizer mixing and the stability of fertilizer transportation []. Examining such systems using coupled computational fluid dynamics and discrete element models can enhance the reliability and reproducibility of performance outcomes []. Additional innovations include a seedling-protection weeding end-effector for maize that couples deep learning detection with sliding-mode control to enforce a minimum tool-to-crop clearance. Field trials report an approximately eighty-eight percent weed-removal rate with only minimal crop injury []. In line with the design concept of agricultural machinery that integrates multiple operational functions, one-pass seeders have been developed that can loosen soil between rows, adjust seeding rate and depth, and apply fertilizer simultaneously []. Electrohydraulic depth control and flow monitoring ensure accurate in-row and between-row seeding under field conditions []. Improved harvesters and material-handling machines further reduce mechanical damage to crops [,]. In the threshing process of high-moisture maize, comparing whole maize ears with pre-split maize ears shows that dividing the ears into smaller sections changes stress paths and support conditions, reducing kernel breakage rates by around 70% when moisture content ranges between 25% and 37% []. This Special Issue also presents work on improved agricultural knotters featuring a double-fluted disk design, where redesigning to double synclastic fluted disks through combined simulation and experimental evaluation has enhanced the bundling performance of rice straw [].
Another notable development is the design of modified harvesters for severely lodged sugarcane. For canes that become lodged, a structurally improved segmental harvester reduces field loss to about one to two percent by stabilizing pickup, transfer, and roller conveyance. Prototype testing through orthogonal experiments demonstrates its effectiveness for severely lodged cane, showing a significant improvement compared with conventional harvesters, which typically experience losses between 15% and 20% []. When traditional farming equipment is used to harvest large volumes of fruit, the fruits frequently bump into each other, causing damage to their appearance. This is particularly problematic for delicate fruits, which are easily harmed and can lose market value as a result []. This Special Issue also introduces techniques for picking fragile fruits in greenhouse environments, including a compliant cable-driven arm with a two-stage fuzzy controller and visual servoing system that synchronizes cable extension and bending to match peduncle geometry, thereby improving harvesting success []. This Special Issue further covers medium-to-large farm machinery, focusing on operational safety, energy efficiency, powertrain systems, soil compaction management, and electrification. Safety assessments for agricultural front-end loaders include both powertrain and structural aspects. Transmission shift quality, fatigue risk, and operational stability are evaluated, and reinforcement of mounting structures is recommended to maintain durability during lifting and dumping operations. A dynamic model developed using measured center of mass and vehicle geometry helps to reduce the risk and expense of rollover testing for small tracked vehicles [,]. Validation against critical side-tilt angle and minimum turning radius shows very small errors, enabling practical limits on slope and turning to be specified for safe operation []. Field-based predictive models of specific fuel consumption in rotary tillage operations provide contour charts that guide the selection of travel speed, power-take-off settings, power levels, and step lengths. This approach achieves fuel savings without compromising tillage quality []. Within power-split drivetrains for agricultural tractors, binary logic transmission units are integrated into hydromechanical continuously variable transmissions. Simulation models, verified through wet clutch test bench data, confirm accurate oil pressure behavior and realistic transmission response. Generalized regression neural networks optimized through advanced algorithms are used to calibrate clutch parameters, resulting in smooth shifts, controlled thermal load, and repeatable parameterization workflows []. Because tractor power consumption and soil compaction are interrelated [], emergence rates for crops such as canola and overall growth performance have been quantified against soil penetration resistance, shear strength, water content, and bulk density. Cross-site trials across sandy loam, silt clay, and clay soils have established soil-specific thresholds, indicating that zero or minimal compaction is preferable []. Higher compaction increases draft and fuel demand and inhibits biomass accumulation. Moreover, low-pressure compaction equipment can reduce yield losses caused by soil degradation. Developing reliable soil–tire interaction prediction models is therefore essential []. Laboratory methods are used to determine initial soil-compaction parameters, which are then refined with field data to improve prediction accuracy. The final calibrated model supports field assessments under different moisture and load conditions []. In the area of electrified agricultural machinery, operating at low speed with high torque, along with fluctuating loads and frequent starts, increases the likelihood of inter-turn short circuits. A refined fault-severity estimation method tuned by probabilistic optimization provides accurate results under both constant and variable speed and load, thereby reducing false shutdowns and enabling effective preventive maintenance [].
Overall, the research presented in this Special Issue reflects a continuous process from design to verification in modern agricultural machinery systems. It begins with mechanism design informed by field geometry and crop diversity, advances to sensing and control methods validated under practical field conditions, and culminates in performance indicators that evaluate the feasibility of field operations. Applied across cropping systems with diverse crops, terrains, and operational scenarios, these studies demonstrate reproducible engineering methods that can be transferred to different cultivation targets and environments. Collectively, these contributions advance the standardization of on-site equipment operations and lay the foundation for data sharing and the integration of heterogeneous modules, thereby enabling comparable, reproducible designs and verifiable outcomes. The collected studies provide a valuable reference for both researchers and industry professionals, guiding future innovation, comparison, and application in this rapidly advancing area of agricultural machinery engineering.

Funding

No external funding was received for this research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ucgul, M.; Chang, C.L. Design and application of agricultural equipment in tillage systems. Agriculture 2023, 13, 790. [Google Scholar] [CrossRef]
  2. Wang, B.; Du, X.; Wang, Y.; Mao, H. Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery. Int. J. Agric. Biol. Eng. 2024, 17, 27–36. [Google Scholar] [CrossRef]
  3. Saiz-Rubio, V.; Rovira-Más, F. From smart farming towards agriculture 5.0: A review on crop data management. Agronomy 2020, 10, 207. [Google Scholar] [CrossRef]
  4. Reddy, R. Innovations in agricultural machinery: Assessing the impact of advanced technologies on farm efficiency. J. Artif. Intell. Big Data 2022, 2, 1. [Google Scholar] [CrossRef]
  5. Xie, D.; Chen, L.; Liu, L.; Chen, L.; Wang, H. Actuators and sensors for application in agricultural robots: A review. Machines 2022, 10, 913. [Google Scholar] [CrossRef]
  6. Chang, C.L.; Chen, H.W.; Ke, J.Y. Robust guidance and selective spraying based on deep learning for an advanced four-wheeled farming robot. Agriculture 2023, 14, 57. [Google Scholar] [CrossRef]
  7. Gao, J.; Jin, Z.; Ai, A. The optimized design of soil-touching parts of a greenhouse humanoid weeding shovel based on strain sensing and DEM–ADAMS coupling simulation. Sensors 2024, 24, 868. [Google Scholar] [CrossRef] [PubMed]
  8. Zhou, S.; He, F.; Deng, G.; Dai, Y.; Wang, X.; Yan, B.; Pan, D. Design and experimentation of a height-adjustable management platform for pineapple fields. Agriculture 2025, 15, 1420. [Google Scholar] [CrossRef]
  9. Li, J.; Zhong Lu, H.; Yang, Z.; Lü, E. Orchard facilities and equipment for litchi and longan in China. In Proceedings of the 2011 ASABE Annual International Meeting, Louisville, KY, USA, 7–10 August 2011; American Society of Agricultural and Biological Engineers: St. Joseph, MO, USA, 2011; p. 1. [Google Scholar]
  10. Li, Z.; Li, C.; Zeng, Y.; Mai, C.; Jiang, R.; Li, J. Design and realization of an orchard operation-aid platform: Based on planting patterns and topography. Agriculture 2024, 15, 48. [Google Scholar] [CrossRef]
  11. Wang, S.; Song, J.; Qi, P.; Yuan, C.; Wu, H.; Zhang, L.; He, X. Design and development of orchard autonomous navigation spray system. Front. Plant Sci. 2022, 13, 960686. [Google Scholar] [CrossRef]
  12. Yu, H.; Ding, Y.; Fu, X.; Liu, H.; Jin, M.; Yang, C.; Dou, X. A solid fertilizer and seed application rate measuring system for a seed-fertilizer drill machine. Comput. Electron. Agric. 2019, 162, 836–844. [Google Scholar] [CrossRef]
  13. Farooqui, N.A.; Haleem, M.; Khan, W.; Ishrat, M. Precision agriculture and predictive analytics: Enhancing agricultural efficiency and yield. In Intelligent Techniques for Predictive Data Analytics; Wiley-IEEE Press: Hoboken, NJ, USA; pp. 171–188.
  14. Li, M.; Chang, X.; Gu, Y.; Wang, P.; Shang, S. Design of dynamic deep sowing system for peanut planter with double-loop feedback fuzzy PID control. Agriculture 2025, 15, 808. [Google Scholar] [CrossRef]
  15. Wu, F.; Li, S.; He, Y.; Song, M.; Ma, F.; Teng, X.; Liang, Y. Optimized design and performance of pre-cutting vibrating sugarcane sett metering device. Sugar Tech. 2023, 25, 210–222. [Google Scholar] [CrossRef]
  16. Yan, B.; Liu, H.; He, F.; Deng, G.; Zheng, S.; Cui, Z.; Li, B. Analysis and testing of pre-cut sugarcane seed stalk sawing performance parameters. Agriculture 2024, 14, 953. [Google Scholar] [CrossRef]
  17. Zhang, L.; Yuan, W.; Jin, C.; Feng, Y.; Liu, G.; Hu, Y. Research progress on key mechanical components of the pneumatic centralized fertilizer discharge system. Appl. Sci. 2024, 14, 3884. [Google Scholar] [CrossRef]
  18. Dong, W.; Zhang, X.; Jiang, Z.; Hu, X.; Ge, Y.; Zhang, L. Study on structure design and parameter optimization of diversion rifled feeder based on CFD–DEM. Agriculture 2025, 15, 351. [Google Scholar] [CrossRef]
  19. Li, Y.; Qu, Y.; Fang, Y.; Yang, J.; Lu, Y. Design and experiment of autonomous shield-cutting end-effector for dual-zone maize field weeding. Agriculture 2025, 15, 1549. [Google Scholar] [CrossRef]
  20. Squires, N.R.W.; Haggar, R.J.; Elliott, J.G. A one-pass seeder for introducing grasses, legumes and fodder crops into swards. J. Agric. Eng. Res. 1979, 24, 199–208. [Google Scholar] [CrossRef]
  21. Zhou, L.; Ma, Y.; Zhou, H.; Niu, K.; Zhao, B.; Wei, L.; Zhang, W. Design and test of sowing depth measurement and control system for no-till corn seeder based on integrated electro-hydraulic drive. Appl. Sci. 2023, 13, 5823. [Google Scholar] [CrossRef]
  22. Ampatzidis, Y.G.; Vougioukas, S.G.; Whiting, M.D.; Zhang, Q. Applying the machine repair model to improve efficiency of harvesting fruit. Biosyst. Eng. 2014, 120, 25–33. [Google Scholar] [CrossRef]
  23. Zhou, L.; Jin, S.; Wang, J.; Zhang, H.; Shi, M.; Zhou, H. 3D positioning of Camellia oleifera fruit-grabbing points for robotic harvesting. Biosyst. Eng. 2024, 246, 110–121. [Google Scholar] [CrossRef]
  24. Wang, G.; Jin, C.; Zhang, M.; Wu, C.; Tang, Q.; Yang, Y. Reducing grain damage in moist corn threshing via corncob division. Agriculture 2024, 14, 1648. [Google Scholar] [CrossRef]
  25. Yin, J.; Chen, Z.; Lv, S.; Wu, H.; Gao, Y.; Wu, L. Design and fatigue life analysis of the rope-clamping drive mechanism in a knotter. Agriculture 2024, 14, 1254. [Google Scholar] [CrossRef]
  26. Jiang, J.; Han, X.; Liu, Q.; Xu, H.; Wu, T.; Feng, J.; Zou, X.; Li, Y. Structural improvement of sugarcane harvester for reducing field loss when harvesting lodged canes. Agriculture 2025, 15, 1759. [Google Scholar] [CrossRef]
  27. Li, Z.; Thomas, C. Quantitative evaluation of mechanical damage to fresh fruits. Trends Food Sci. Technol. 2014, 35, 138–150. [Google Scholar] [CrossRef]
  28. Chang, C.L.; Huang, C.C. Design and implementation of an AI-based robotic arm for strawberry harvesting. Agriculture 2024, 14, 2057. [Google Scholar] [CrossRef]
  29. Cameron, J.T.; Brennan, S.N. Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover. Ph.D. Thesis, Pennsylvania State University, University Park, PA, USA, 2005. [Google Scholar]
  30. Wang, F.; Chen, Y. Vehicle rollover propensity detection based on a mass-center-position metric: A continuous and completed method. IEEE Trans. Veh. Technol. 2019, 68, 8652–8662. [Google Scholar] [CrossRef]
  31. Yang, Y.J.; Jang, M.K.; Nam, J.S. Dynamic simulation model of miniature tracked forestry tractor for overturning and rollover safety evaluation. Agriculture 2024, 14, 1991. [Google Scholar] [CrossRef]
  32. Kim, S.-J.; Jang, M.-K.; Hwang, S.-J.; Lee, W.S.; Nam, J.-S. Development of a prediction model for specific fuel consumption in rotary tillage based on actual operation. Agriculture 2024, 14, 1993. [Google Scholar] [CrossRef]
  33. Li, W.; Cheng, Z.; Yang, M. Configurational comparison of a binary logic transmission unit applicable to agricultural tractor hydro-mechanical continuously variable transmissions and its wet clutch optimization design based on an improved general regression neural network. Agriculture 2025, 15, 877. [Google Scholar] [CrossRef]
  34. Shaheb, M.R.; Venkatesh, R.; Shearer, S.A. A review on the effect of soil compaction and its management for sustainable crop production. J. Biosyst. Eng. 2021, 46, 417–439. [Google Scholar] [CrossRef]
  35. Owusu-Sekyere, E.; Chen, Y. The effect of varying compaction levels on soil dynamic properties and the growth of canola (Brassica napus L.). Agriculture 2024, 14, 1976. [Google Scholar] [CrossRef]
  36. Zeng, H.; Zhao, C.; Chen, S.; Xu, W.; Zang, M. Numerical simulations of tire–soil interactions: A comprehensive review. Arch. Comput. Methods Eng. 2023, 30, 4801–4829. [Google Scholar] [CrossRef]
  37. Shokanbi, A.; Jasoliya, D.; Untaroiu, C. Parameter identification of soil material model for soil compaction under tire loading: Laboratory vs. in-situ cone penetrometer test data. Agriculture 2025, 15, 2142. [Google Scholar] [CrossRef]
  38. Wang, M.; Lai, W.; Sun, P.; Li, H.; Song, Q. Severity estimation of inter-turn short-circuit fault in PMSM for agricultural machinery using Bayesian optimization and enhanced convolutional neural network architecture. Agriculture 2024, 14, 2214. [Google Scholar] [CrossRef]
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