From Planting to Harvesting: The Role of Agricultural Machinery in Crop Cultivation

The modernization of crop production is inextricably linked to the continuous advancement of agricultural machinery. Across the entire cultivation chain—from soil preparation and planting to field management and harvesting—mechanization innovations have fundamentally enhanced efficiency, precision, and sustainability. Today’s agricultural machinery integrates intelligent systems, precision tools, and adaptive technologies, laying the foundation for smarter and more responsive farming systems.

Efficient tillage provides the basis for robust crop growth. Studies of vibration characteristics in no-tillage planter row units on wheat stubble fields have revealed how mechanical structure directly influences soil disturbance and sowing depth consistency [1]. Such research enables the development of optimized tillage units that balance minimal soil disruption with improved seedbed conditions. Additionally, profiling and root-trimming mechanisms have emerged to better suit complex field conditions. For instance, a profiled root-trimming device has been designed to support pull–cut harvesting operations in root crop systems [2]. These devices enhance field adaptability and lay the foundation for mechanized root crop production.

Planting technology is at the heart of achieving high and stable crop yields. The development of seed metering devices—especially those using discrete element method (DEM) and multi-body dynamics (MBD) simulation—has advanced precision seeding across various crops. For rice cultivation, a side-filled precision hole seeding mechanism demonstrated through MBD–DEM simulations effectively improves seed placement and consistency [3]. Moreover, vibrational dynamics analyses of precision seeders help understand the operational stability of different seeding mechanisms under field conditions [4]. In high-value crops such as quinoa, innovations like the air-suction metering device address seed metering challenges by controlling cluster-hole effects and improving singulation performance [5]. Further innovations include seed rope designs whose phenotypic and tensile properties directly influence root morphology and seedling health [6], reflecting the increasing convergence of agronomy and mechanization. In sugarcane production, analysis of pre-cut stalk sawing parameters enables optimized cutting performance, ensuring high-quality seed stalks for the next planting cycle [7]. Trenching and planting integration, particularly in transverse sugarcane planters [8], has optimized work efficiency and reduced labor intensity for large-scale sugarcane planting operations.

Field management technologies have expanded rapidly, especially under the push for precision agriculture and sustainability. Intelligent sprayers are becoming more targeted and efficient, supported by vision-based segmentation models. For instance, FCB-YOLOv8s-Seg enables instance-level recognition of malignant weeds in soybean fields for targeted herbicide application, reducing input waste and environmental burden [9]. In large-scale field sprayers, suspension systems must maintain stability across varied terrains. A variable universe fuzzy neural network approach—optimized by artificial fish swarm algorithms—has enhanced the active suspension performance of large sprayers, promoting better droplet targeting and system safety [10]. Additionally, energy management in hybrid tractors is seeing significant strides. A dynamic programming-based strategy for OS-ECVT hybrid systems not only improves energy efficiency but also extends the operational life of hybrid powertrains [11], reflecting the growing integration of renewable energy in agricultural machinery.

The harvesting stage remains one of the most complex in mechanization. For ratoon rice, a rigid–flexible coupled rod tooth threshing device—optimized using MBD–DEM—has demonstrated high threshing efficiency while preserving stubble integrity [12]. A complementary study analyzed the interaction between preharvest threshing devices and rice at maturity, using DEM and bench tests to verify the threshing effectiveness and crop protection [13]. Similarly, sorghum threshing technologies based on sparse–dense curved tooth structures address grain detachment challenges and support dual-crop processing [14]. In root and tuber crops such as sweet potatoes, understanding the physical properties of varieties at different harvest times aids in designing adaptive harvesting equipment [15]. Sunflower harvesting systems have also been refined with the addition of efficient cleaning devices to reduce foreign material and enhance seed quality [16]. Meanwhile, adaptive profiling headers—capable of contour tracking—have been proposed to support harvesting under undulating field conditions [17]. In fruit harvesting, robotic systems incorporating 3D obstacle-avoidance path planning based on improved ant colony algorithms have enabled selective harvesting while minimizing fruit damage [18]. Cotton harvesting also benefits from hybrid hydraulic–mechanical transmission systems, which improve traction adaptability and operational efficiency in variable soil conditions [19]. Lastly, in unharvested rice field detection, the TCNet model—fusing transformer and convolutional networks—offers precise recognition capabilities for guiding autonomous harvesting operations [20].

As agricultural machinery evolves from mechanized assistance to intelligent collaboration, future advancements are set to revolutionize every aspect of crop production, driven by the convergence of AI, edge computing, robotics, multi-source perception, and green energy. Embedding artificial intelligence and lightweight neural networks into machinery enables real-time decision-making at the edge, empowering autonomous seeders, sprayers, and harvesters to analyze terrain, soil, and crop conditions on-site and dynamically adjust their operations. Multi-modal sensor fusion—integrating visual, LiDAR, ultrasonic, inertial, and UWB data—enhances perception, allowing machines to identify crop rows, assess plant health, navigate variable terrain, and selectively operate on individual plants with precision. Agricultural robotics is advancing toward coordinated multi-machine systems, where fleets of autonomous units carry out intra- and inter-row tasks collaboratively through wireless communication and swarm intelligence, improving field coverage and energy efficiency. At the same time, the concept of digital twins enables virtual modeling and optimization of machinery performance based on simulations of mechanical components and biological interactions, supporting predictive maintenance and real-time calibration. Electrification is also accelerating, with hybrid and fully electric drivetrains offering lower emissions, higher energy efficiency, and compatibility with renewable energy sources like solar-powered charging. These trends align with global goals of sustainability and resource conservation. Moreover, a deeper integration between agronomic knowledge and mechanical design is shaping the next generation of crop-specific machinery; examples include seed metering systems optimized for root structure or ratoon rice harvesters preserving stubble height for regrowth. Modular and terrain-adaptive machinery—featuring adjustable tracks, intelligent suspensions, and quick-attach multi-function platforms—will support operations across diverse landscapes, particularly in hilly and mountainous regions. Simultaneously, machinery will become a core component of digital agriculture ecosystems, collecting vast field data on soil health, crop status, and machine operation. These data feed into cloud-based farm management platforms and AI-driven decision support systems to inform real-time recommendations for irrigation, fertilization, harvesting, and logistics. Such data-driven systems not only increase productivity and profitability but also promote sustainable resource use. Ultimately, agricultural machinery is transitioning from tool-based operation to intelligent, integrated platforms capable of perception, decision-making, execution, and learning in closed-loop systems. These developments will enable smarter, greener, and more adaptive farming, redefining the role of agricultural equipment as an active partner in modern food production systems.