Mulched drip irrigation is an important water-saving production system for soybean cultivation in arid and semi-arid regions. However, soybean materials harvested from this system exhibit specific physical characteristics, including relatively dry pods and seeds and high stem moisture content. This study characterized the physical and mechanical properties of soybean seeds at the harvest stage and established a discrete element method (DEM) parameter system for harvesting simulation. Key properties and contact behaviors were experimentally measured, and DEM parameters were calibrated and validated through physical tests and virtual simulations. The results showed that the calibrated parameters accurately reproduced the macroscopic stacking behavior of soybean seeds, with the angle of repose validation showing a relative error of only 0.545%. The proposed parameter system considers the geometric and mechanical characteristics of harvest-stage soybean seeds and improves the applicability of DEM simulations for harvesting process analysis. This study provides digital support for optimizing soybean harvesting machinery, reducing seed damage, and improving harvest quality in water-efficient production systems.

Crop rotation is a key practice for improving soil health, reducing chemical inputs, and ensuring sustainable agricultural productivity. This review synthesizes research from major agricultural regions worldwide, including North America, Europe, South America, South Asia, and Africa, with additional case studies from China to illustrate regional applications. This study presents a streamlined framework that integrates climate adaptability, crop combinations, yield-enhancing mechanisms, technological support, and regional optimization. By analyzing plant–soil–microbe interactions—including improvements in soil physical structure, nutrient cycling, microbial processes, and suppression of pests and diseases—we elucidate how rotation systems enhance the yield of subsequent crops. Precision agriculture technologies, such as variable-rate fertilization and remote sensing, have been shown to improve resource use efficiency and reduce labor input in certain cropping systems. Diversified crop rotations can substantially offset direct greenhouse gas emissions through enhanced soil carbon sequestration. The design of region-specific recommended rotation patterns should follow the principles of resource matching, stable yield and efficiency enhancement, and sustainability. This framework provides a practical reference for designing region-specific rotation systems and advancing sustainable agricultural development.

Sorghum is an important multi-purpose crop used for food, feed, brewing, and bioenergy. However, mechanised harvesting is hindered by its tall stature, complex panicle morphology, small and fragile grains, high-moisture stems and leaves, and susceptibility to lodging at maturity. This review provides a PRISMA-guided systematic literature search and narrative synthesis of mechanised sorghum harvesting from crop adaptability to intelligent equipment. The main literature search covered publications from January 1990 to May 2026 and included Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, Google Scholar, and other relevant sources. A total of 1928 records were identified, and 190 studies were finally included in the qualitative synthesis after duplicate removal, title-and-abstract screening, and full-text assessment. The review analyses how plant morphology, panicle exsertion, physical and mechanical properties, maturity stage, moisture content, varietal differences, and lodging affect harvesting suitability. The applicable conditions for segmented harvesting, panicle harvesting, direct grain harvesting, and multi-purpose coordinated harvesting are compared under different crop, regional, and machinery conditions. Key technological advances are synthesised in relation to header feeding, threshing and cleaning, straw management, and operating-parameter optimisation. Recent developments in machine vision, condition monitoring, adaptive control, DEM and CFD-DEM simulation, and digital twins are also assessed. The analysis shows that high-quality mechanised sorghum harvesting requires the coordinated optimisation of crop traits, harvesting methods, machine structures, operating parameters, and digital sensing–control feedback. The main contribution of this review is to establish an integrated crop–machine–operation framework for identifying technical constraints, comparing harvesting routes, and guiding the development of specialised, low-loss, low-breakage, and intelligent sorghum harvesting systems.

Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.

Inland waters such as lakes, reservoirs, rivers, fish ponds, and confined hydraulic structures impose turbidity, shallow-water acoustic multipath, dense boundaries, dynamic biological interference, and limited communication on underwater robots. These coupled constraints can simultaneously degrade sensing, localization, mapping, planning, and control. This structured narrative review synthesizes peer-reviewed English-language evidence on underwater navigation, with emphasis on multisensor fusion localization and the transfer of localization uncertainty into risk-aware planning. Visual, acoustic, inertial, velocity, depth, and external positioning measurements are compared by complementarity, observability, degradation modes, and integration cost. Filtering, sliding-window optimization, factor graphs, estimator switching, and hybrid model- and data-driven approaches are evaluated according to accuracy, real-time performance, robustness, and localization credibility. The review examines how covariance, sensing quality, collision probability, energy, platform dynamics, and task requirements affect maps, path costs, safety margins, trajectory tracking, and degraded operation. Evidence from simulation, public datasets, hardware-in-the-loop tests, tanks, and real waters is synthesized conditionally because study platforms, environments, ground truth, and metrics are heterogeneous. Reliable inland-water autonomy requires diagnosable heterogeneous sensing, integrity-aware localization, and coordinated feedback among localization, planning, and control. Mission-level evaluation should consider data validity, fault recovery, and safe completion rather than average localization error or shortest path alone.

Agricultural weeding robots are progressing from single-function automation toward end-to-end autonomy that integrates task planning, field navigation, weed perception, precision execution, and operational support. This review synthesises 217 sources identified through structured searches of major multidisciplinary, engineering, and Chinese literature databases, supplemented by citation tracking, followed by documented screening, evidence-use coding, technical-theme mapping, and a review-defined L1–L5 validation-coverage assessment. Three main findings emerge. First, reported technical evidence is concentrated at physical closed-loop feasibility in real agricultural environments rather than at complete production service. Among 157 studies with classifiable technical validation, 78 (49.7%) attained L3 as their highest level, whereas only one met the predefined criteria for complete production service and sustained operation with quantified supervision; that study concerned area-based mowing rather than target-level selective weeding. A separate analysis of 49 direct robotic-weeding technical studies showed the same concentration at L3. In a scenario-based boundary-robustness analysis, the number of direct studies attaining L4 or above increased from 1/49 under the strict criteria to 2/49 and 3/49 under two deliberately relaxed scenarios, while L3 remained the dominant highest level. Second, validation depth is uneven: navigation, trajectory control, and several execution modalities have stronger real-field evidence than global task planning, weed perception, fault diagnosis, and autonomous replenishment. Third, the principal system bottleneck is increasingly cross-module rather than purely algorithmic: spatial, temporal, and task-state inconsistencies can propagate through interfaces into missed or repeated treatment, crop damage, unfinished tasks, additional downtime, and human intervention. The review therefore argues for validation centred on recoverable complete production tasks, with explicit reporting of task completion, agronomic outcomes, faults and recovery, replenishment, downtime, resource use, and supervision.