Mechanization and Intelligent Technologies for Ginger Harvesting: Evolution, Frontiers, and Prospects

Driven by agricultural labor shortages and rising quality requirements, ginger harvesting increasingly demands high-throughput, low-damage operations and a reliable supply chain. This review summarizes harvesting modes and harvester types used in ginger production, with emphasis on critical process modules: digging and lifting, soil disintegration and cleaning, vine cutting and anti-tangling, gentle conveying, and collection. We compare major technical routes in terms of field capacity, control of soil and foreign materials, damage mitigation, and reliability under continuous operation, and identify the conditions under which each route performs best. Drawing on advances in harvesting systems for other root and bulb crops, we outline transferable approaches for intelligent sensing, precision control, and system-level integration. We then propose an online monitoring and closed-loop regulation framework for strongly coupled conditions, such as heavy clay soils, plastic-mulch residues, and vine interference. Key bottlenecks include limited cross-regional adaptability, persistent trade-offs between low damage and high throughput, cost constraints on intelligent functions, and the lack of shared datasets and standardized evaluation protocols. Future progress should be anchored in integrated equipment sets and supporting operating specifications, guided by multi-source sensing-based quality indicators and interpretable control strategy libraries, to reduce harvest losses, stabilize marketable quality, improve operational efficiency, and enable scalable adoption.

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Publication Details

Journal
AgriEngineering
Published
2026-03-15
DOI
https://doi.org/10.3390/agriengineering8030112
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Mechanization and Intelligent Technologies for Ginger Harvesting: Evolution, Frontiers, and Prospects

Gongpu Wang, Baoliang Peng, Guangyu Xue, Lianglong Hu et al.
AgriEngineering
Smart Agriculture and AI
article

Mechanization and Intelligent Technologies for Ginger Harvesting: Evolution, Frontiers, and Prospects

Gongpu Wang, Baoliang Peng, Guangyu Xue, Lianglong Hu, X. Y. Shen, Yanhua Zhang, Wenhao Zheng
article en

Abstract

Driven by agricultural labor shortages and rising quality requirements, ginger harvesting increasingly demands high-throughput, low-damage operations and a reliable supply chain. This review summarizes harvesting modes and harvester types used in ginger production, with emphasis on critical process modules: digging and lifting, soil disintegration and cleaning, vine cutting and anti-tangling, gentle conveying, and collection. We compare major technical routes in terms of field capacity, control of soil and foreign materials, damage mitigation, and reliability under continuous operation, and identify the conditions under which each route performs best. Drawing on advances in harvesting systems for other root and bulb crops, we outline transferable approaches for intelligent sensing, precision control, and system-level integration. We then propose an online monitoring and closed-loop regulation framework for strongly coupled conditions, such as heavy clay soils, plastic-mulch residues, and vine interference. Key bottlenecks include limited cross-regional adaptability, persistent trade-offs between low damage and high throughput, cost constraints on intelligent functions, and the lack of shared datasets and standardized evaluation protocols. Future progress should be anchored in integrated equipment sets and supporting operating specifications, guided by multi-source sensing-based quality indicators and interpretable control strategy libraries, to reduce harvest losses, stabilize marketable quality, improve operational efficiency, and enable scalable adoption.

AgriEngineeringVol. 8(3)
Agricultural Information Institute (CN), Chinese Academy of Agricultural Sciences (CN), Ministry of Agriculture and Rural Affairs (CN), Nanjing Institute of Agricultural Mechanization (CN)
Chinese Academy of Agricultural Sciences
Openalex Percentile: Top 20%
Smart Agriculture and AI
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