Xin Lu

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.

Agrivoltaic systems configure photovoltaic power generation and agricultural production within the same land space, providing a new pathway for alleviating the conflict between energy development and farmland conservation; however, array shading and structural constraints also alter crop growth and agricultural equipment operating environments. Following the PRISMA process, this review searched studies published from 2010 to 2026 in the Web of Science Core Collection, Scopus, and China National Knowledge Infrastructure, and ultimately included 206 publications. The review focuses on array-induced environmental reconfiguration, equipment adaptation, light–thermal sensing and prediction, and coordinated agrivoltaic operation. Existing evidence indicates that the environmental effects of agrivoltaic systems are clearly influenced by climate and array configuration; in representative vertical or tracking systems, annual-scale photosynthetically active radiation decreased by approximately 11% to 34%. Mismatch among module height, row spacing, and implement width reduces field-operation efficiency, which fell to approximately 45% under severe mismatch in some experiments; elevated arrays also cause GNSS signal attenuation and increase the difficulty of continuous positioning beneath the panels. Because existing studies differ considerably in site conditions, evaluation indicators, and validation periods, the above results mainly reflect representative performance ranges. Matching criteria between photovoltaic arrays and agricultural machinery have not yet been established, long-term field-measured data in complex field environments are insufficient, model adaptability across regions is limited, and coordinated scheduling of agricultural production and photovoltaic operation and maintenance remains inadequate. Overall, coordinated design of arrays and agricultural machinery, multi-sensor fusion navigation, environmental prediction corrected for array structure, and hierarchical scheduling under safety constraints are the main development directions for intelligent coordinated operation of agrivoltaic systems.

Grapes intended for fresh consumption, winemaking, raisin production and processing differ markedly in quality requirements and tolerance to harvest damage, making a universally suitable mechanised harvesting pathway unlikely. This review synthesises low-damage grape harvesting from an end use-oriented sustainability perspective. A structured literature search and thematic narrative synthesis covered studies mainly published from January 2000 to May 2026, with 184 studies included. The evidence indicates that harvesting suitability is determined by the compatibility among end use, grape and bunch traits, vineyard architecture, damage tolerance, labour and operating constraints, and downstream requirements. Manual and assisted harvesting remain preferable where selective whole-bunch handling and marketability dominate; vibration-based bulk harvesting provides greater capacity for wine and processing grapes in compatible vineyards; dry-on-vine (DOV) collection is closely coupled with raisin-specific drying systems; and selective robotic harvesting remains promising but requires stronger commercial vineyard validation. Across these pathways, higher automation does not inherently provide greater sustainability because reductions in labour demand may be offset by quality loss, capital and energy requirements, or insufficient system utilisation. The principal contribution of this review is an end use-specific framework linking vineyard design, harvesting pathway, damage and loss mechanisms, and environmental, economic and social sustainability considerations. Because experimental conditions, damage definitions and reporting metrics remain heterogeneous, the available literature supports context-dependent qualitative comparison rather than a unified quantitative sustainability ranking.

Strawberry production is economically important but remains highly dependent on labour-intensive harvesting. The delicate texture, irregular distribution, and non-uniform maturity of strawberry fruit create substantial challenges for mechanised and robotic operations. This review examines the development of strawberry-harvesting technologies from the broader perspectives of crop value, cultivation management, harvesting methods, robotic systems, post-harvest handling, and sustainable production. The nutritional and economic significance of strawberries is first outlined, followed by an analysis of cultivation environments, production patterns, and crop-management practices that influence fruit accessibility and robotic operation. The historical transition from manual harvesting to mechanised and intelligent harvesting is then reviewed. Particular attention is given to the principal technologies of strawberry-harvesting robots, including mobile platforms, robotic manipulators, path planning and obstacle avoidance, end-effectors, visual recognition, multispectral sensing, and software control. Robotic systems designed for elevated and ridge-based cultivation are also compared to clarify the influence of cultivation layout on platform configuration and harvesting strategy. In addition, the integration of harvesting with fruit transfer, post-harvest handling, and sustainable cultivation is discussed. The reviewed studies indicate that effective robotic harvesting depends on the coordinated design of cultivation systems, perception, motion planning, compliant manipulation, and system control. Future research should prioritise robust perception under occlusion, low-damage harvesting, improved operational speed, scenario adaptability, cost reduction, and closer integration between agronomic practices and robotic design.

Long-term intensive agriculture has contributed to soil compaction, carbon depletion, nutrient imbalances, and disruption of microbial ecological processes, collectively constraining multiple soil functions relevant to agricultural sustainability. Crop straw return is widely considered a potential strategy for alleviating these constraints. However, existing studies and reviews have often evaluated direct straw return, straw-derived biochar, and straw-based compost separately or through individual soil indicators, limiting understanding of how biomass transformation, amendment properties, and site conditions jointly shape soil responses. To address this gap, this review comparatively synthesizes the reported mechanisms, outcomes, limitations, and potential application contexts of these three strategies within a soil multifunctionality framework. The reviewed literature is characterized by substantial heterogeneity in soil type, climate, feedstock, amendment preparation, application rate, experimental duration, and management conditions; therefore, the direction, magnitude, and persistence of reported effects require context-specific interpretation. Direct straw return was often associated with changes in soil structure, labile-carbon availability, water retention, and microbial activity, although these responses varied with straw type, incorporation depth, moisture conditions, decomposition rate, and nitrogen availability. Biochar was frequently linked to carbon stabilization, sorption, nutrient retention, and pH buffering, but the magnitude of these effects varied with feedstock properties, pyrolysis conditions, application rate, soil characteristics, and climatic context. Compost was commonly associated with increases in nutrient availability and microbial activity, whereas its performance varied with maturity, raw-material composition, salinity and pathogen risks, and field management. These comparisons suggest that the three strategies should not be assumed to be functionally equivalent, although their effects may overlap and potential combinations may be beneficial under some conditions. Based on patterns identified across the reviewed literature, we synthesize an interpretive framework linking dominant soil constraints with amendment properties and targeted soil functions. This literature-derived framework is intended to organize context-dependent evidence and support adaptive straw-return management rather than provide a universal prescription. Future research should prioritize standardized soil multifunctionality indicators, long-term multi-site comparisons, and integrated assessments of agronomic benefits, carbon persistence, nutrient losses, greenhouse gas emissions, and economic feasibility.

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.

Ball vegetables (such as cabbage, Chinese cabbage, broccoli, etc.) hold an important position in the vegetable industry due to their unique morphology and diverse applications and are widely favored by both consumers and the market. However, the harvesting of Ball vegetables poses significant challenges to agricultural production and market supply. Traditional manual harvesting struggles to meet the rapid demands of large-scale cultivation, primarily due to its high labor intensity and time-consuming nature, compounded by the increasingly prominent issues of aging and shortage of agricultural labor in recent years. As an alternative, intelligent harvesting robot technology, through integration with optimized cropping practices, innovations in preservation techniques, and improvements in processing workflows, offers an effective solution for expanding market planting areas and enhancing production efficiency. However, such harvesting robots still require further optimization and improvement in terms of adaptability, operational efficiency, and damage control. To systematically review the research progress and current status of this field, this study employs a bibliometric analysis approach to evaluate the current performance characteristics of various types of heading vegetable harvesting robots, aiming to provide a reference for future technological developments. This review analyzes solutions suitable for low-damage, high-quality harvesting of Ball vegetables in modern agriculture from five dimensions: identification and localization, row-following mechanisms, cutting mechanisms, pulling and conveying mechanisms, and leaf-removal mechanisms. It also summarizes the main challenges currently facing harvesting equipment, including the complexity of harvest targets, diversification of crop varieties and cultivation patterns, and harvest-induced damage to Ball vegetables. Finally, this review provides a future outlook on heading vegetable harvesting from four perspectives: research on the characteristics of Ball vegetables, investigation into harvest-induced damage mechanisms, improvement in machinery adaptability, and enhancement in equipment versatility and intelligence.

Due to topographic and climatic constraints, a large proportion of grain is cultivated in relatively flat areas, where it is highly susceptible to lodging caused by natural disasters or improper field management. However, research on the full-process intelligent harvesting of lodged grain remains limited. This paper aims to examine the current state of the grain industry and its level of mechanization, with a focus on the challenges in mechanized harvesting of lodged grain, including the lack of suitable equipment, high harvest losses, and low operational efficiency. The article provides a comprehensive review of the key technologies critical to achieving fully automated intelligent harvesting, including the acquisition of lodging information, combine harvester operations, and the development of intelligent modular systems. It also summarizes recent advancements in cutting-edge mechanized grain harvesting equipment. Additionally, the article presents recommendations for the future development and research of mechanized grain harvesting technologies, focusing on grain lodging feature recognition, equipment modularization, and the integration of intelligent technologies. The aim is to offer valuable insights for advancing progress in this field.

Electrostatic droplets can enhance deposition on target leaves. To comprehensively investigate the main factors affecting droplet adhesion and splashing after electrostatic droplet impact on pepper leaves, a specialized experimental platform was developed to capture the impact behavior of charged droplets on pepper leaves. The following four variables were examined: the applied voltage of the electrode ring, droplet size, impact velocity, and the relative distance from the impact point to the leaf tip (expressed as a percentage of the total leaf length). The results showed that all four factors had significant effects on droplet adhesion. The effect sizes (partial η²) of the four factors, in descending order, were impact velocity (0.935), applied voltage (0.907), impact position (0.895), and droplet size (0.505). Compared with non-electrostatic droplets, the increase in the maximum spreading factor of charged droplets was positively correlated with droplet size, impact velocity, and applied voltage but showed no significant correlation with the relative distance to the leaf tip. The critical velocity for splashing of electrostatic droplets decreased to a certain extent but increased with higher applied voltages. The influence of the four factors on droplet splashing decreases in the following order: impact velocity (odds ratio ≫ 1), charging voltage (odds ratio = 1.246), droplet size (odds ratio = 1.023), and impact position (odds ratio < 1). Among these, impact velocity has the most significant effect on splashing behavior. This study reveals the adhesion and splashing mechanisms of electrostatic droplets on flexible pepper leaves, providing theoretical support for the design of electrostatic spraying systems and the selection of operational parameters. The findings offer a scientific basis for optimizing droplet deposition, minimizing splash losses, and improving application efficiency in precision spraying.