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.
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.
The harvesting of fruit and vegetable crops has long been plagued by prominent issues such as high labor costs, low harvesting efficiency, and high fruit damage rates. The application of object recognition technology has enabled harvesting robots to identify, detect, and locate crops in certain agricultural scenarios, achieving a degree of automated harvesting. However, these systems still suffer from shortcomings such as poor robustness in complex environments, insufficient generalization ability, and high model deployment costs, which significantly limit their large-scale application in agricultural harvesting equipment. This paper comprehensively reviews recent literature in the field of fruit and vegetable target recognition. It summarizes how current research focuses on the implementation principles and directions for the improvement of mainstream methods—including digital image processing, traditional machine learning, and deep learning—while also identifying the remaining issues and challenges facing current technology in terms of algorithmic model real-time performance, robustness, and generalization ability. In the future, target recognition technology is expected to achieve breakthroughs through approaches such as multimodal feature fusion, large-scale models, and semi-supervised learning, evolving toward higher accuracy, faster processing speeds, and easier deployment, thereby providing technical support for the large-scale implementation of smart agriculture.
Embodied intelligence offers a system-level paradigm for improving the adaptability of robotic harvesting in complex horticultural environments by coupling perception, decision-making, and physical interaction. This review systematically analyzes recent advances in embodied intelligence for robotic harvesting. A search of the Web of Science Core Collection covering January 2020 to August 2026 identified 294 records, of which 100 studies were included after screening. Based on the reviewed literature, a literature-grounded Four-Tier framework is proposed, comprising Multimodal Active Sensing, Semantic Context Recognition and Understanding, Knowledge-Driven Decision-Making and Experience Evolution, and Skilled and Compliant Execution. The review shows that embodied approaches have improved individual capabilities across perception, decision-making, and manipulation; for example, representative studies reported over 12% improvement in detection accuracy, reduction in fruit damage from 8.2% to 2.0%, and 89% retention in continual learning. However, reliable autonomous harvesting remains constrained by cross-module uncertainty, real-time crop–robot interaction, continual adaptation, and the lack of standardized benchmarks. Future research should emphasize physically grounded multimodal closed-loop integration, experience-driven adaptation, standardized evaluation, and crop–robot co-design.
Yue Sun, Wei-Dong Jia, Yun-Fei Wang et al.· Applied Sciences· 0 citations
Flower picking is a labor intensive process heavily in the floriculture industry, and it remains predominantly manual. With the increasing shortage of agricultural labor and the continuous rise in labor costs, the sustainable development of the flower industry is facing severe challenges. Therefore, the using of robotic systems to picking flower plants has become an inevitable trend in the development of modern agriculture. This paper presents a systematic review of recent advances in flower picking robot, with a particular focus on system architecture and core enabling technologies. The core technologies are visual perception and target recognition, motion control and path planning, and flexible, non-destructive end effector design. The research status and limitations of these key technologies are analyzed, with special attention to their applications in both open field and controlled facility environments. Current challenges include low recognition accuracy under complex lighting and occlusion, insufficient end effector adaptability to diverse varieties, and low overall picking efficiency. Future directions include multiple modal sensing integrated with artificial intelligence, bionic and soft robotic end effectors, and multiple robot collaborative system. Finally, based on the perspectives of design and application, the review provides future research directions toward industrialization, with a particular focus on key enabling technologies of these technologies.
Z. Du, Peng-Yu Yi, Xin Jin et al.· Frontiers in Plant Science· 0 citations
Microalgal bioproducts hold significant promise for sustainable applications, but the high production costs associated with cultivation systems remain a major barrier to commercialization. The optimal growth of microalgae depends on a range of factors, including light, temperature, pH, nutrients, and mixing. Each strain of microalgae has unique cultivation requirements, necessitating tailored systems to maximize yield. Traditional cultivation systems, such as open ponds, photobioreactors, and fermentation tanks, each have distinct advantages and limitations. Open ponds rely on natural conditions but are difficult to control, while photobioreactors offer precise environmental control for maximum yield, albeit at higher costs. Fermentation tanks, which exploit heterotrophic growth, reduce operating expenses compared to photobioreactors. Machine learning‐driven automation and smart technologies offer a promising strategy to reduce these costs by optimizing cultivation processes through electronic data logging, automated control, and minimal human intervention. This review explores the role of automation in the cultivation, harvesting, extraction, and post‐processing of microalgae. It examines recent advancements in automated technologies and their potential to enhance the efficiency and scalability of microalgal bioproduct production. Key considerations for automation in microalgae production include system requirements, technological advancements, and integration across various stages of the production chain. The review emphasizes the mechanical aspects of automation, focusing on technologies that can be automated to improve yield and reduce operational costs, and highlights the need for further innovation in this field to facilitate the widespread commercialization of microalgae‐based products.
Angela Paul Peter, W. Mahari, S. Y. M. Lim et al.· GCB Bioenergy· 0 citations
Labor shortages and the push for higher greenhouse efficiency have accelerated interest in automated harvesting. However, the development of a universal harvesting robot has been constrained by large variations in crop architecture, especially plant height. In this review, a height-based stratification of greenhouse harvesting robots and transferable high-wire crop harvesters is presented, covering ground-level crops (<0.6 m, e.g., strawberry), medium-height crops (0.6–1.5 m, e.g., tomato), and high-wire crops (>1.5 m, e.g., trellised cucumber). For each height layer, key design features, technical progress, prototype performance, and common obstacles—including fruit occlusion, mechanical crop damage, unreliable operation, and high commercial costs—are analyzed. Future efforts should target intelligent perception, soft end-effectors, and height-specific solutions (swarm robotics for ground crops, modular hybrid designs for medium crops, infrastructure co-design for high-wire crops). By using plant height as the primary stratification criterion, a design-oriented framework is provided, distinct from conventional crop-type or mechanism-based categorizations.
