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市場調查報告書

商品編碼

1980076

機器人收割市場預測：至 2034 年－全球分析（按機器人類型、移動方式、部署方式、組件、作物類型、農場規模、技術、最終用戶和地區分類）

Robotic Harvesting Market Forecasts to 2034 - Global Analysis By Robot Type, Mobility Type (Ground-Based Robots, Aerial Harvesting Robots, and Hybrid Systems), Deployment Mode, Component, Crop Type, Farm Size, Technology, End User, and By Geography

出版日期: 2026年03月11日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的研究，預計到 2026 年，全球機器人收割市場將達到 32 億美元，並在預測期內以 19.4% 的複合年成長率成長，到 2034 年達到 135 億美元。

機器人收割系統利用先進的機器人技術、電腦視覺和人工智慧，能夠自主、精準、細緻地辨識、分類和收割農作物。這些技術不僅解決了農業勞動力嚴重短缺的問題，也提高了收割效率，減少了食物廢棄物。市場涵蓋了各種類型的機器人和移動平台，適用於從井然有序的果園到複雜的田間作物等各種農業環境，從根本上改變了全球傳統的農業作業方式。

農業勞動力持續短缺

已開發國家的農民長期面臨季節性人工收割工人短缺的困境，因此迫切需要自動化替代方案。移民政策、農業勞動力老化以及其他就業領域的競爭，都在收割季急需快速反應之際，導致勞動力供應減少。機器人收割系統能夠持續運作，不受勞動力短缺的影響，並滿足高峰期的需求。勞動力短缺造成的作物未收割帶來的經濟損失日益凸顯，也使得自動化投資更具吸引力。隨著人事費用上升和技術價格下降，投資回收期不斷縮短，使得機器人解決方案對先進的農業企業而言極具經濟吸引力。

初始投資規模

機器人收割系統前期投入成本高昂，這仍然是許多農業企業，特別是資金籌措有限的中小型農場的一大障礙。先進感測器、專用機械手臂和人工智慧系統的應用，使得機器人收割系統的價格遠高於傳統收割設備。計算投資報酬率需要考慮季節性使用模式，因為昂貴的設備一年中的大部分時間都處於閒置狀態。資金籌措挑戰、技術壽命的不確定性以及快速的技術創新週期所導致的過時擔憂，都進一步加劇了購買決策的複雜性，使得儘管機器人收割系統具有顯著的營運優勢，但仍難以推廣應用。

電腦視覺和人工智慧的進展

機器學習演算法的快速發展使得收割機器人能夠執行以往無法自動化的複雜辨識與分類任務。現代視覺系統能夠以接近人類的精確度判斷作物成熟度、偵測缺陷並避開茂密的枝葉。基於海量農業資料集訓練的深度學習模型，在各種作物和生長條件下不斷提升效能。這些技術進步正在突破現有限制，拓展可處理的作物範圍，並開闢新的市場領域，例如特種作物、果園和葡萄園——這些領域以往因操作要求精細而難以實現自動化。

作物多樣性與環境複雜性

生長季節、區域條件和作物品種固有的生物變異性為針對特定參數設計的機器人系統帶來了挑戰。天氣現象會改變作物的位置，葉片密度會隨季節波動，動態的田間環境中還會出現意想不到的障礙。與受控的工業環境不同，農業環境具有無限的變異性，這阻礙了僵化的自動化方法。操作不當造成的作物損傷會降低商品產量，抵銷節省勞力帶來的利益。這些操作風險會阻礙那些無法容忍收割失敗的生產者，並減緩商業性系統的廣泛應用，因此需要進行大量的田間測試和客製化。

新冠疫情的影響：

新冠疫情暴露了農業勞動力供應鏈的嚴重脆弱性，並大大推動了人們對機器人收割解決方案的興趣。出行限制和勞動力流動受限導致季節性工人無法在收穫高峰期抵達農場，造成了前所未有的作物損失。社交距離的要求降低了收割機的密度，進一步限制了人工收割能力。這些干擾迫使生產者重新考慮先前認為無利可圖的自動化投資。疫情的持續影響包括提高了人們對供應鏈韌性的認知，以及加快了農業部門（此前該部門一直在抵制變革）的技術應用步伐。

在預測期內，全自動收割機器人細分市場預計將成為最大的細分市場。

在預測期內，全自動收割機器人預計將成為最大的細分市場。全自動收割機器人無需持續的人工運作，即可自主導航田間作業，識別成熟待收割的作物，並獨立完成收割過程。這些先進的系統整合了精密的感測器、人工智慧和精準操作技術，能夠模擬人類在整個收割過程中的決策。它們能夠跨多個班次長時間運作，從而最大限度地提高設備利用率和投資回報率。隨著勞動力短缺問題日益嚴重以及技術可靠性不斷提高，全自動解決方案在大規模農業生產中的應用正在加速，並透過提升營運效率，推動其在該細分市場中佔據主導地位。

在預測期內，空中收割機器人（無人機）領域預計將呈現最高的複合年成長率。

預計在預測期內，空中收割機器人（無人機）領域將實現最高成長率。空中收割機器人利用無人機平台運作，能夠到達地面設備難以觸及的崎嶇地形和樹冠深處，進行作物收割。這些飛行系統在果園、棚架式葡萄園和坡地農田等地面作業困難或容易造成破壞的地區具有獨特的優勢。快速部署能力使得在作物達到最佳成熟度時進行精準收割成為可能。電池技術、飛行穩定性以及輕型機械手臂的不斷進步，正在拓展空中收割的能力。隨著種植者逐漸認知到3D收割方式的變革潛力，基於無人機的農業系統試驗正在加速進行。

市佔率最大的地區：

在預測期內，北美地區預計將保持最大的市場佔有率，這主要得益於嚴重的農業勞動力短缺、大規模的農業生產以及強大的創新生態系統。美國和加拿大的生產者正面臨日益嚴格的移民限制和季節性工人數量的減少，這使得自動化需求變得迫切。來自農業院校和創業投資創投的大量研究經費正在加速技術開發和實地測試。早期採用者正在展示機器人收割在特種作物方面的實用性，並建立概念驗證，這將有助於在整個預測期內推動全部區域的廣泛應用。

預計複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於農業勞動力老化、技術快速普及以及政府主導的現代化政策。日本和韓國正透過先進的機器人技術研究主導區域發展，這些研究旨在應用於高價值園藝作物。中國的大規模農業部門正面臨勞動力向都市區流失的問題，這催生了對自動化的需求，而國家政策支援和國內製造業能力將解決這個問題。東南亞出口導向農業國家正在投資收割技術，以維持國際競爭力。區域人口趨勢和經濟發展軌跡的結合，正在創造巨大的成長機會。

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企業概況
- 對其他市場參與者（最多 3 家公司）進行全面分析
- 主要參與者（最多3家公司）的SWOT分析
區域細分
- 主要國家的市場估算和預測，以及根據客戶需求量身定做的複合年成長率（註：需要進行可行性測試）。
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- 根據主要參與者的產品系列、地理覆蓋範圍和策略聯盟進行基準分析。

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 荷蘭
- 比利時
- 瑞典
- 瑞士
- 波蘭
- 其他歐洲國家
亞太地區
- 中國
- 日本
- 印度
- 韓國
- 澳洲
- 印尼
- 泰國
- 馬來西亞
- 新加坡
- 越南
- 其他亞太國家
南美洲
- 巴西
- 阿根廷
- 哥倫比亞
- 智利
- 秘魯
- 其他南美國家
世界其他地區（RoW）
- 中東
  - 沙烏地阿拉伯
  - 阿拉伯聯合大公國
  - 卡達
  - 以色列
  - 其他中東國家
- 非洲
  - 南非
  - 埃及
  - 摩洛哥
  - 其他非洲國家

第14章策略市場資訊

工業價值網路和供應鏈評估
空白區域和機會地圖
產品演進與市場生命週期分析
通路、經銷商和打入市場策略的評估

第15章產業趨勢與策略舉措

併購
夥伴關係、聯盟和合資企業
新產品發布和認證
擴大生產能力和投資
其他策略舉措

第16章：公司簡介

John Deere
CNH Industrial NV
AGCO Corporation
Trimble Inc.
Harvest CROO Robotics LLC
FFRobotics Ltd.
Octinion NV
Dogtooth Technologies Ltd.
Abundant Robotics, Inc.
Root AI, Inc.
Vision Robotics Corporation
Advanced Farm Technologies Inc.
Ripe Robotics Pty Ltd
Agrobot（Harvest Automation）
Yamaha Motor Co., Ltd.

簡介目錄圖表

Product Code: SMRC34240

According to Stratistics MRC, the Global Robotic Harvesting Market is accounted for $3.2 billion in 2026 and is expected to reach $13.5 billion by 2034 growing at a CAGR of 19.4% during the forecast period. Robotic harvesting systems utilize advanced robotics, computer vision, and artificial intelligence to autonomously identify, select, and harvest crops with precision and care. These technologies address critical labor shortages in agriculture while improving harvest efficiency and reducing food waste. The market encompasses various robot types and mobility platforms designed for diverse agricultural environments, from structured orchards to complex field crops, fundamentally transforming traditional farming operations worldwide.

Market Dynamics:

Driver:

Persistent agricultural labor shortages

Farmers across developed economies face chronic difficulties securing seasonal workers for manual harvesting operations, creating urgent demand for automated alternatives. Immigration policies, aging agricultural workforces, and competing employment sectors have reduced labor availability precisely when harvest windows demand rapid action. Robotic harvesting systems operate continuously without fatigue, addressing peak season demands regardless of worker availability. The economic impact of unharvested crops due to labor shortages increasingly justifies automation investments, with payback periods shrinking as labor costs rise and technology prices decline, making robotic solutions economically compelling for progressive agricultural operations.

Restraint:

High initial capital investment

Substantial upfront costs for robotic harvesting systems remain prohibitive for many agricultural operations, particularly small and medium-sized farms with limited capital access. Advanced sensors, specialized manipulators, and artificial intelligence systems contribute to price points exceeding traditional harvesting equipment by significant margins. Return on investment calculations must account for seasonal usage patterns that leave expensive equipment idle throughout much of the year. Financing challenges, uncertain technology lifespans, and rapid innovation cycles creating obsolescence concerns further complicate purchasing decisions, slowing adoption despite compelling operational benefits.

Opportunity:

Advancements in computer vision and AI

Rapid progress in machine learning algorithms enables harvesting robots to perform increasingly complex identification and selection tasks previously impossible to automate. Modern vision systems distinguish crop ripeness, detect defects, and navigate dense foliage with accuracy approaching human capabilities. Deep learning models trained on vast agricultural datasets continuously improve performance across diverse crop varieties and growing conditions. These technological advances expand addressable crop types beyond current limitations, opening new market segments in specialty crops, orchards, and vineyards where delicate handling requirements have historically resisted automation.

Threat:

Crop variability and environmental complexity

Inherent biological variability across growing seasons, regional conditions, and crop varieties challenges robotic systems designed for specific parameters. Weather events alter crop positioning, foliage density changes throughout seasons, and unexpected obstacles appear in dynamic field environments. Unlike controlled industrial settings, agricultural environments present infinite variability that confounds rigid automation approaches. Crop damage from improper handling reduces marketable yields, potentially offsetting labor savings. These operational risks create hesitation among growers who cannot afford harvest failures, requiring extensive field testing and customization that slows widespread commercial deployment.

Covid-19 Impact:

The COVID-19 pandemic exposed critical vulnerabilities in agricultural labor supply chains, dramatically accelerating interest in robotic harvesting solutions. Travel restrictions and workforce mobility limitations prevented seasonal workers from reaching farms during peak harvest periods, creating unprecedented crop losses. Social distancing requirements reduced harvesting crew densities, further constraining manual capacity. These disruptions forced growers to reconsider automation investments previously deemed marginal. The pandemic's lasting impact includes heightened awareness of supply chain resilience and accelerated technology adoption timelines across agricultural sectors previously resistant to change.

The Fully Autonomous Harvesting Robots segment is expected to be the largest during the forecast period

The Fully Autonomous Harvesting Robots segment is anticipated to be the largest during the forecast period. Fully autonomous harvesting robots operate without continuous human intervention, navigating fields, identifying harvest-ready crops, and performing picking operations independently. These sophisticated systems integrate advanced sensors, artificial intelligence, and precision manipulation technologies to replicate human decision-making throughout the harvest process. Their ability to operate extended hours across multiple shifts maximizes equipment utilization and return on investment. Large-scale agricultural operations increasingly adopt fully autonomous solutions as labor shortages intensify and technology reliability improves, driving this segment's dominant market position through operational efficiency gains.

The Aerial Harvesting Robots (Drone-Based) segment is expected to have the highest CAGR during the forecast period

The Aerial Harvesting Robots (Drone-Based) segment is expected to register the highest growth rate during the forecast period. Aerial harvesting robots operating from drone platforms access crops in challenging terrain and canopy positions inaccessible to ground-based equipment. These flying systems offer unique advantages for orchard crops, trellised vineyards, and sloped agricultural lands where ground navigation proves difficult or damaging. Rapid deployment capabilities enable targeted harvesting of high-value crops during optimal ripeness windows. Ongoing advancements in battery technology, flight stability, and lightweight manipulators expand aerial harvesting capabilities. Agricultural experimentation with drone-based systems accelerates as growers recognize the transformative potential of three-dimensional harvesting approaches.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by severe agricultural labor shortages, large-scale farming operations, and strong technology innovation ecosystems. United States and Canadian growers face intensifying immigration enforcement and declining seasonal worker availability, creating urgent automation demands. Substantial research funding through agricultural universities and private venture capital accelerates technology development and field testing. Early adopter farmers demonstrate robotic harvesting viability across specialty crops, establishing proof-of-concept that drives broader regional adoption throughout the forecast period.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by aging agricultural workforces, rapid technology adoption, and government modernization initiatives. Japan and South Korea lead regional development with advanced robotics research applied to high-value horticultural crops. China's massive agricultural sector faces labor migration to urban centers, creating automation imperatives addressed through national policy support and domestic manufacturing capabilities. Southeast Asian nations with export-oriented agriculture invest in harvesting technology to maintain global competitiveness. Regional demographic trends and economic development trajectories combine to create exceptional growth opportunities.

Key players in the market

Some of the key players in Robotic Harvesting Market include John Deere, CNH Industrial N.V., AGCO Corporation, Trimble Inc., Harvest CROO Robotics LLC, FFRobotics Ltd., Octinion NV, Dogtooth Technologies Ltd., Abundant Robotics, Inc., Root AI, Inc., Vision Robotics Corporation, Advanced Farm Technologies Inc., Ripe Robotics Pty Ltd, Agrobot, and Yamaha Motor Co., Ltd.

Key Developments:

In January 2026, Dogtooth announced a strategic shift to 3D-printed hybrid manufacturing for its fruit-picking robots. By using Selective Laser Sintering (SLS), the company successfully reduced the lead time for sensor integration and customized robotic arm covers, allowing for more rapid field iterations in berry harvesting.

In August 2025, John Deere unveiled its 2026 automated combine line, featuring advanced AI that adjusts ground speed based on terrain and crop density. New "hands-free" capabilities include AutoTrac controlling the head during turns and a camera system on the unloading auger that automatically aligns with grain carts to minimize waste.

In February 2022, Yamaha Motor Co., Ltd. acquired Robotics Plus to form Yamaha Agriculture, Inc. This new entity focuses on scaling the Prospr autonomous hybrid vehicle, which supports autonomous spraying and is developing harvesting attachments for specialty crops like grapes and apples.

Robot Types Covered:

Fully Autonomous Harvesting Robots
Semi-Autonomous Harvesting Robots
Teleoperated Harvesting Robots

Mobility Types Covered:

Ground-Based Robots
Aerial Harvesting Robots (Drone-Based)
Hybrid Systems

Deployment Modes Covered:

On-Premise Farm Deployment
Robotics-as-a-Service (RaaS)

Components Covered:

Hardware
Software
Services

Crop Types Covered:

Fruits
Vegetables
Field Crops
Specialty Crops
Other Crops

Farm Sizes Covered:

Small Farms
Medium Farms
Large Commercial Farms

Technologies Covered:

Computer Vision-Based Systems
AI & Machine Learning-Based Robots
Sensor-Based Harvesting Robots
Soft Robotics Harvesting Systems
End-Effector Technologies
Multi-Robot Coordination Systems

End Users Covered:

Commercial Farms
Contract Farming Companies
Agricultural Cooperatives
Agri-Tech Companies
Research Institutions
Other End Users

Regions Covered:

North America
- United States
- Canada
- Mexico
Europe
- United Kingdom
- Germany
- France
- Italy
- Spain
- Netherlands
- Belgium
- Sweden
- Switzerland
- Poland
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Vietnam
- Rest of Asia Pacific
South America
- Brazil
- Argentina
- Colombia
- Chile
- Peru
- Rest of South America
Rest of the World (RoW)
- Middle East
Saudi Arabia
United Arab Emirates
Qatar
Israel
Rest of Middle East
- Africa
South Africa
Egypt
Morocco
Rest of Africa

What our report offers:

Market share assessments for the regional and country-level segments
Strategic recommendations for the new entrants
Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
Strategic recommendations in key business segments based on the market estimations
Competitive landscaping mapping the key common trends
Company profiling with detailed strategies, financials, and recent developments
Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

Company Profiling
- Comprehensive profiling of additional market players (up to 3)
- SWOT Analysis of key players (up to 3)
Regional Segmentation
- Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
Competitive Benchmarking
- Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

1 Executive Summary

1.1 Market Snapshot and Key Highlights
1.2 Growth Drivers, Challenges, and Opportunities
1.3 Competitive Landscape Overview
1.4 Strategic Insights and Recommendations

2 Research Framework

2.1 Study Objectives and Scope
2.2 Stakeholder Analysis
2.3 Research Assumptions and Limitations
2.4 Research Methodology
- 2.4.1 Data Collection (Primary and Secondary)
- 2.4.2 Data Modeling and Estimation Techniques
- 2.4.3 Data Validation and Triangulation
- 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

3.1 Market Definition and Structure
3.2 Key Market Drivers
3.3 Market Restraints and Challenges
3.4 Growth Opportunities and Investment Hotspots
3.5 Industry Threats and Risk Assessment
3.6 Technology and Innovation Landscape
3.7 Emerging and High-Growth Markets
3.8 Regulatory and Policy Environment
3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

4.1 Porter's Five Forces Analysis
- 4.1.1 Supplier Bargaining Power
- 4.1.2 Buyer Bargaining Power
- 4.1.3 Threat of Substitutes
- 4.1.4 Threat of New Entrants
- 4.1.5 Competitive Rivalry
4.2 Market Share Analysis of Key Players
4.3 Product Benchmarking and Performance Comparison

5 Global Robotic Harvesting Market, By Robot Type

5.1 Fully Autonomous Harvesting Robots
5.2 Semi-Autonomous Harvesting Robots
5.3 Teleoperated Harvesting Robots

6 Global Robotic Harvesting Market, By Mobility Type

6.1 Ground-Based Robots
6.2 Aerial Harvesting Robots (Drone-Based)
6.3 Hybrid Systems

7 Global Robotic Harvesting Market, By Deployment Mode

7.1 On-Premise Farm Deployment
7.2 Robotics-as-a-Service (RaaS)

8 Global Robotic Harvesting Market, By Component

8.1 Hardware
- 8.1.1 Robotic Arms
- 8.1.2 Sensors & Cameras
- 8.1.3 Grippers & End Effectors
- 8.1.4 Navigation Systems
8.2 Software
- 8.2.1 AI Algorithms
- 8.2.2 Farm Management Integration Software
8.3 Services
- 8.3.1 Installation & Integration
- 8.3.2 Maintenance & Support
- 8.3.3 Training & Consulting

9 Global Robotic Harvesting Market, By Crop Type

9.1 Fruits
- 9.1.1 Berries
- 9.1.2 Apples
- 9.1.3 Citrus Fruits
- 9.1.4 Grapes
- 9.1.5 Other Fruits
9.2 Vegetables
- 9.2.1 Tomatoes
- 9.2.2 Cucumbers
- 9.2.3 Leafy Greens
- 9.2.4 Peppers
- 9.2.5 Other Vegetables
9.3 Field Crops
- 9.3.1 Grains
- 9.3.2 Oilseeds
- 9.3.3 Pulses
9.4 Specialty Crops
9.5 Other Crops

10 Global Robotic Harvesting Market, By Farm Size

10.1 Small Farms
10.2 Medium Farms
10.3 Large Commercial Farms

11 Global Robotic Harvesting Market, By Technology

11.1 Computer Vision-Based Systems
11.2 AI & Machine Learning-Based Robots
11.3 Sensor-Based Harvesting Robots
11.4 Soft Robotics Harvesting Systems
11.5 End-Effector Technologies
11.6 Multi-Robot Coordination Systems

12 Global Robotic Harvesting Market, By End User

12.1 Commercial Farms
12.2 Contract Farming Companies
12.3 Agricultural Cooperatives
12.4 Agri-Tech Companies
12.5 Research Institutions
12.6 Other End Users

13 Global Robotic Harvesting Market, By Geography

13.1 North America
- 13.1.1 United States
- 13.1.2 Canada
- 13.1.3 Mexico
13.2 Europe
- 13.2.1 United Kingdom
- 13.2.2 Germany
- 13.2.3 France
- 13.2.4 Italy
- 13.2.5 Spain
- 13.2.6 Netherlands
- 13.2.7 Belgium
- 13.2.8 Sweden
- 13.2.9 Switzerland
- 13.2.10 Poland
- 13.2.11 Rest of Europe
13.3 Asia Pacific
- 13.3.1 China
- 13.3.2 Japan
- 13.3.3 India
- 13.3.4 South Korea
- 13.3.5 Australia
- 13.3.6 Indonesia
- 13.3.7 Thailand
- 13.3.8 Malaysia
- 13.3.9 Singapore
- 13.3.10 Vietnam
- 13.3.11 Rest of Asia Pacific
13.4 South America
- 13.4.1 Brazil
- 13.4.2 Argentina
- 13.4.3 Colombia
- 13.4.4 Chile
- 13.4.5 Peru
- 13.4.6 Rest of South America
13.5 Rest of the World (RoW)
- 13.5.1 Middle East
  - 13.5.1.1 Saudi Arabia
  - 13.5.1.2 United Arab Emirates
  - 13.5.1.3 Qatar
  - 13.5.1.4 Israel
  - 13.5.1.5 Rest of Middle East
- 13.5.2 Africa
  - 13.5.2.1 South Africa
  - 13.5.2.2 Egypt
  - 13.5.2.3 Morocco
  - 13.5.2.4 Rest of Africa

14 Strategic Market Intelligence

14.1 Industry Value Network and Supply Chain Assessment
14.2 White-Space and Opportunity Mapping
14.3 Product Evolution and Market Life Cycle Analysis
14.4 Channel, Distributor, and Go-to-Market Assessment

15 Industry Developments and Strategic Initiatives

15.1 Mergers and Acquisitions
15.2 Partnerships, Alliances, and Joint Ventures
15.3 New Product Launches and Certifications
15.4 Capacity Expansion and Investments
15.5 Other Strategic Initiatives

16 Company Profiles

16.1 John Deere
16.2 CNH Industrial N.V.
16.3 AGCO Corporation
16.4 Trimble Inc.
16.5 Harvest CROO Robotics LLC
16.6 FFRobotics Ltd.
16.7 Octinion NV
16.8 Dogtooth Technologies Ltd.
16.9 Abundant Robotics, Inc.
16.10 Root AI, Inc.
16.11 Vision Robotics Corporation
16.12 Advanced Farm Technologies Inc.
16.13 Ripe Robotics Pty Ltd
16.14 Agrobot (Harvest Automation)
16.15 Yamaha Motor Co., Ltd.

簡介目錄圖表

List of Tables

Table 1 Global Robotic Harvesting Market Outlook, By Region (2023-2034) ($MN)
Table 2 Global Robotic Harvesting Market Outlook, By Robot Type (2023-2034) ($MN)
Table 3 Global Robotic Harvesting Market Outlook, By Fully Autonomous Harvesting Robots (2023-2034) ($MN)
Table 4 Global Robotic Harvesting Market Outlook, By Semi-Autonomous Harvesting Robots (2023-2034) ($MN)
Table 5 Global Robotic Harvesting Market Outlook, By Teleoperated Harvesting Robots (2023-2034) ($MN)
Table 6 Global Robotic Harvesting Market Outlook, By Mobility Type (2023-2034) ($MN)
Table 7 Global Robotic Harvesting Market Outlook, By Ground-Based Robots (2023-2034) ($MN)
Table 8 Global Robotic Harvesting Market Outlook, By Aerial Harvesting Robots (Drone-Based) (2023-2034) ($MN)
Table 9 Global Robotic Harvesting Market Outlook, By Hybrid Systems (2023-2034) ($MN)
Table 10 Global Robotic Harvesting Market Outlook, By Deployment Mode (2023-2034) ($MN)
Table 11 Global Robotic Harvesting Market Outlook, By On-Premise Farm Deployment (2023-2034) ($MN)
Table 12 Global Robotic Harvesting Market Outlook, By Robotics-as-a-Service (RaaS) (2023-2034) ($MN)
Table 13 Global Robotic Harvesting Market Outlook, By Component (2023-2034) ($MN)
Table 14 Global Robotic Harvesting Market Outlook, By Hardware (2023-2034) ($MN)
Table 15 Global Robotic Harvesting Market Outlook, By Robotic Arms (2023-2034) ($MN)
Table 16 Global Robotic Harvesting Market Outlook, By Sensors & Cameras (2023-2034) ($MN)
Table 17 Global Robotic Harvesting Market Outlook, By Grippers & End Effectors (2023-2034) ($MN)
Table 18 Global Robotic Harvesting Market Outlook, By Navigation Systems (2023-2034) ($MN)
Table 19 Global Robotic Harvesting Market Outlook, By Software (2023-2034) ($MN)
Table 20 Global Robotic Harvesting Market Outlook, By AI Algorithms (2023-2034) ($MN)
Table 21 Global Robotic Harvesting Market Outlook, By Farm Management Integration Software (2023-2034) ($MN)
Table 22 Global Robotic Harvesting Market Outlook, By Services (2023-2034) ($MN)
Table 23 Global Robotic Harvesting Market Outlook, By Installation & Integration (2023-2034) ($MN)
Table 24 Global Robotic Harvesting Market Outlook, By Maintenance & Support (2023-2034) ($MN)
Table 25 Global Robotic Harvesting Market Outlook, By Training & Consulting (2023-2034) ($MN)
Table 26 Global Robotic Harvesting Market Outlook, By Crop Type (2023-2034) ($MN)
Table 27 Global Robotic Harvesting Market Outlook, By Fruits (2023-2034) ($MN)
Table 28 Global Robotic Harvesting Market Outlook, By Berries (2023-2034) ($MN)
Table 29 Global Robotic Harvesting Market Outlook, By Apples (2023-2034) ($MN)
Table 30 Global Robotic Harvesting Market Outlook, By Citrus Fruits (2023-2034) ($MN)
Table 31 Global Robotic Harvesting Market Outlook, By Grapes (2023-2034) ($MN)
Table 32 Global Robotic Harvesting Market Outlook, By Other Fruits (2023-2034) ($MN)
Table 33 Global Robotic Harvesting Market Outlook, By Vegetables (2023-2034) ($MN)
Table 34 Global Robotic Harvesting Market Outlook, By Tomatoes (2023-2034) ($MN)
Table 35 Global Robotic Harvesting Market Outlook, By Cucumbers (2023-2034) ($MN)
Table 36 Global Robotic Harvesting Market Outlook, By Leafy Greens (2023-2034) ($MN)
Table 37 Global Robotic Harvesting Market Outlook, By Peppers (2023-2034) ($MN)
Table 38 Global Robotic Harvesting Market Outlook, By Other Vegetables (2023-2034) ($MN)
Table 39 Global Robotic Harvesting Market Outlook, By Field Crops (2023-2034) ($MN)
Table 40 Global Robotic Harvesting Market Outlook, By Grains (2023-2034) ($MN)
Table 41 Global Robotic Harvesting Market Outlook, By Oilseeds (2023-2034) ($MN)
Table 42 Global Robotic Harvesting Market Outlook, By Pulses (2023-2034) ($MN)
Table 43 Global Robotic Harvesting Market Outlook, By Specialty Crops (2023-2034) ($MN)
Table 44 Global Robotic Harvesting Market Outlook, By Other Crops (2023-2034) ($MN)
Table 45 Global Robotic Harvesting Market Outlook, By Farm Size (2023-2034) ($MN)
Table 46 Global Robotic Harvesting Market Outlook, By Small Farms (2023-2034) ($MN)
Table 47 Global Robotic Harvesting Market Outlook, By Medium Farms (2023-2034) ($MN)
Table 48 Global Robotic Harvesting Market Outlook, By Large Commercial Farms (2023-2034) ($MN)
Table 49 Global Robotic Harvesting Market Outlook, By Technology (2023-2034) ($MN)
Table 50 Global Robotic Harvesting Market Outlook, By Computer Vision-Based Systems (2023-2034) ($MN)
Table 51 Global Robotic Harvesting Market Outlook, By AI & Machine Learning-Based Robots (2023-2034) ($MN)
Table 52 Global Robotic Harvesting Market Outlook, By Sensor-Based Harvesting Robots (2023-2034) ($MN)
Table 53 Global Robotic Harvesting Market Outlook, By Soft Robotics Harvesting Systems (2023-2034) ($MN)
Table 54 Global Robotic Harvesting Market Outlook, By End-Effector Technologies (2023-2034) ($MN)
Table 55 Global Robotic Harvesting Market Outlook, By Multi-Robot Coordination Systems (2023-2034) ($MN)
Table 56 Global Robotic Harvesting Market Outlook, By End User (2023-2034) ($MN)
Table 57 Global Robotic Harvesting Market Outlook, By Commercial Farms (2023-2034) ($MN)
Table 58 Global Robotic Harvesting Market Outlook, By Contract Farming Companies (2023-2034) ($MN)
Table 59 Global Robotic Harvesting Market Outlook, By Agricultural Cooperatives (2023-2034) ($MN)
Table 60 Global Robotic Harvesting Market Outlook, By Agri-Tech Companies (2023-2034) ($MN)
Table 61 Global Robotic Harvesting Market Outlook, By Research Institutions (2023-2034) ($MN)
Table 62 Global Robotic Harvesting Market Outlook, By Other End Users (2023-2034) ($MN)