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市場調查報告書
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1766143

全球自動收割機市場:預測至 2032 年-按產品類型、自動化程度、推進方式、運作地點、作物類型、技術、最終使用者和地區進行分析

Autonomous Harvester Market Forecasts to 2032 - Global Analysis By Product Type, Level of Automation, Propulsion Type, Site of Operation, Crop Type, Technology, End User and By Geography

出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據,全球自動收割機市場預計在 2025 年達到 19 億美元,到 2032 年將達到 44 億美元,預測期內的複合年成長率為 12.8%。

自動收割機是一種自走式農業機械,旨在以最少的人工干預完成收割任務。這些機器利用GPS、感測器、電腦視覺和人工智慧導航系統等先進技術,能夠有效率地辨識、收集和加工作物。這提高了生產力,減少了對勞動力的依賴,並確保了農業作業的精準性。自動收割機在大型農場尤其受到重視,因為高效和及時的收割對於獲得最佳產量至關重要。

根據聯合國估計,到 2022 年 11 月中旬,世界人口將達到 80 億,而 1950 年預計為 25 億。

精密農業的採用日益增多

精密農業技術使農民能夠透過數據主導的決策、即時監測和自動化流程來最佳化作物產量,從而提高營運效率。自動收割機與農場管理系統無縫整合,並提供先進的感測器、機器學習功能和即時數據分析,從而提高農業作業的準確性和生產力。此外,這些系統還能增強作物健康監測,最佳化收穫計劃,簡化資源管理,使其成為現代農業企業不可或缺的工具。

缺乏操作先進系統的技術純熟勞工

缺乏操作先進自動收割系統的技術純熟勞工,為農業企業帶來了營運挑戰。這些先進的機器依賴複雜的技術,包括人工智慧、機器學習、感測器和GPS系統,需要專業知識和技術專長才能有效地操作和維護它們。此外,自動收割機的複雜性要求對農業工人進行持續的培訓和技能提升,而許多農業企業由於資源和技術教育計畫有限而難以提供這些培訓和技能。這種技能缺口尤其影響那些無法聘請專業技術人員的中小型農業企業。

勞動力短缺和人事費用上升

農業部門面臨勞動力短缺的重大挑戰,美國農場聯合會估計,光是美國每年就需要僱用約250萬名農場工人。此外,農業人口老化以及年輕一代不願從事體力勞動,也增加了對先進農業機械的需求,這些機械可以減少對勞動力的依賴。此外,自動化收割機可以連續不間斷地作業,確保及時且有效率的收割作業,從而解決勞動力短缺問題,提高作業效率,並降低長期人事費用。

初期資本投入及營運成本高

購買和部署自動收割機所需的巨額前期投資,對於預算有限的中小型農場來說,構成了經濟障礙。這些成本不僅包括機械的購買價格,還包括安裝、設置、與現有農場營運模式的整合以及持續維護相關的費用。此外,人工智慧系統、感測器、GPS 設備和機器學習等先進技術組件也導致營運成本上升,許多農業經營者難以承受。

COVID-19的影響:

新冠疫情對自動收割機市場造成了重大衝擊,供應鏈中斷和短暫的生產延誤影響了設備的供應。然而,由於出行限制和健康擔憂加劇了勞動力短缺,這場危機也加速了自動化農業解決方案的採用。此外,疫情凸顯了減少農業生產中對人力的依賴的重要性,促使那些在不確定時期尋求持續經營的農民對自動收割技術產生了濃厚的興趣。

預計柴油引擎市場在預測期內將佔據最大佔有率

憑藉完善的基礎設施和在農業應用中久經考驗的可靠性,柴油引擎預計將在預測期內佔據最大的市場佔有率。與汽油引擎相比,柴油引擎具有更高的燃油效率,這為農民節省了大量成本,尤其是在燃油價格高漲的地區。此外,全部區域完善的柴油基礎設施確保了全球農業作業的穩定供應和便利性。此外,柴油引擎技術的最新進展,例如共軌缸內直噴系統,在滿足嚴格的排放法規的同時提高了燃油經濟性,使其成為尋求可靠且經濟高效解決方案的農民的首選。

預計水果和蔬菜板塊在預測期內將達到最高複合年成長率

在預測期內,由於對精細作物精準採收的需求日益成長,預計水果和蔬菜領域將呈現最高成長率。這些特殊作物需要小心處理,以最大限度地減少損害並保持其品質,這推動了對配備先進感測器和專為輕柔採收作業設計的演算法的自動採收機的需求。此外,全球對新鮮農產品的需求不斷成長,再加上水果和蔬菜採收的勞動密集性質,為自動化解決方案創造了巨大的機會。此外,即時數據分析功能使這些機器能夠導航複雜的果園佈局並適應不斷變化的採收條件,從而確保最佳效率並將作物損失降至最低。

佔比最大的地區:

由於對研發的大力投資、有利的政府激勵措施以及主要農業技術製造商的存在,預計北美將在預測期內佔據最大的市場佔有率。受大規模農業經營和精密農業農業市場佔有率的廣泛採用的推動,美國佔據了北美大部分市場佔有率。此外,該地區受益於支持機械化農業的先進基礎設施,根據美國農場聯合會的數據,超過 70% 的大型農場使用自走式聯合收割機。此外,全自動系統的推動,加上人工智慧和機器學習技術的快速發展,繼續推動全部區域的市場擴張。

複合年成長率最高的地區:

在預測期內,由於農業方法的快速現代化和對糧食安全的日益擔憂,預計亞太地區將呈現最高的複合年成長率。包括中國、印度和日本在內的國家正積極將人工智慧和物聯網技術應用於農業機械,同時解決農村地區嚴重的勞動力短缺問題。此外,政府透過補貼和機械化計畫推動精密農業的舉措,正在加速全部區域自動收割機的普及。此外,該地區廣闊的農業地理和對現代化的強烈追求,尤其是在印度、中國和印度尼西亞,正在推動全部區域亞太地區的市場擴張。

自動化水平

  • 半自動收割機(駕駛員輔助)
  • 全自動收割機(無人駕駛)

免費客製化服務

本報告的所有訂閱者均可享有以下免費自訂選項之一:

  • 公司簡介
    • 對其他公司(最多 3 家)進行全面分析
    • 主要企業的SWOT分析(最多3家公司)
  • 區域分類
    • 根據客戶興趣對主要國家市場進行估計、預測和複合年成長率(註:基於可行性檢查)
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第 2 章 簡介

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 主要研究資料
    • 二手研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 介紹
  • 驅動程式
  • 限制因素
  • 市場機會
  • 威脅
  • 產品分析
  • 技術分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 企業之間的競爭

第5章全球自動收割機市場(依產品類型)

  • 結合
  • 青貯收割機
  • 割草機
  • 水果採摘機
  • 甘蔗收割機
  • 馬鈴薯收割機
  • 蔬菜收穫機
  • 其他收割機

6. 全球自動收割機市場(依自動化程度)

  • 半自動收割機(輔助駕駛)
  • 全自動收割機(無人駕駛)

7. 全球自動收割機市場(依推進型)

  • 柴油引擎
  • 混合

8. 全球自動收割機市場(按運作)

  • 農事
  • 受控環境農業(CEA)
    • 溫室
    • 室內農場

第9章。全球自動收割機市場(按作物類型)

  • 糧食
  • 水果和蔬菜
  • 棉布
  • 甘蔗
  • 其他作物

第10章。全球自動收割機市場(按技術)

  • GPS/GNSS技術
  • LiDAR/雷達感測器
  • 電腦視覺攝影系統
  • 人工智慧和機器學習
  • 物聯網 (IoT)
  • 邊緣運算
  • 雲端連線和遠端資訊處理

第 11 章全球自動收割機市場(按最終用戶)

  • 大型農場
  • 中型農場
  • 小型農場
  • 農業合作社
  • 合約農業服務

第 12 章全球自動收割機市場(按地區)

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第13章:主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併與收購(M&A)
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第14章 公司簡介

  • John Deere(Deere & Company)
  • CNH Industrial
  • AGCO Corporation
  • Kubota Corporation
  • CLAAS KGaA mbH
  • Yanmar Co., Ltd.
  • Mahindra & Mahindra Ltd.
  • SDF Group
  • Iseki & Co., Ltd.
  • Harvest CROO Robotics
  • Naio Technologies
  • Agrobot
  • Harvest Automation, Inc.
  • Eos Crop Automation
  • Autonomous Solutions, Inc.(ASI)
  • AgEagle Aerial Systems Inc.
  • Raven Industries, Inc.
  • Solinftec
Product Code: SMRC29999

According to Stratistics MRC, the Global Autonomous Harvester Market is accounted for $1.9 billion in 2025 and is expected to reach $4.4 billion by 2032 growing at a CAGR of 12.8% during the forecast period. An autonomous harvester is a self-operating agricultural machine designed to perform harvesting tasks with minimal human intervention. Using advanced technologies such as GPS, sensors, computer vision, and AI-driven navigation systems, these machines can efficiently identify, collect, and process crops. They enhance productivity, reduce labor dependency, and ensure precision in farming operations. Autonomous harvesters are particularly valuable in large-scale farms where efficiency and timely harvesting are critical for optimal yield.

According to the United Nations, the global human population reached 8.0 billion in mid-November 2022, up from an estimated 2.5 billion in 1950.

Market Dynamics:

Driver:

Increasing adoption of precision agriculture

Precision agriculture technologies enable farmers to optimize crop yields through data-driven decision-making, real-time monitoring, and automated processes that enhance operational efficiency. Autonomous harvesters integrate seamlessly with farm management systems, providing advanced sensors, machine learning capabilities, and real-time data analytics that bolster precision and productivity in agricultural operations. Additionally, these systems enhance crop health monitoring, optimize harvesting schedules, and streamline resource management, making them indispensable tools for modern agricultural enterprises.

Restraint:

Lack of skilled workforce for operating advanced systems

The lack of a skilled workforce for operating advanced autonomous harvesting systems creates operational challenges for agricultural enterprises. These sophisticated machines depend on advanced technologies, including artificial intelligence, machine learning, sensors, and GPS systems, which require specialized knowledge and technical expertise to operate and maintain effectively. Moreover, the complexity of autonomous harvesters demands continuous training and upskilling of farm personnel, which many agricultural operations struggle to provide due to limited resources and access to technical education programs. This skills gap particularly affects small and medium-sized farming operations that lack the financial capacity to hire specialized technicians.

Opportunity:

Labor shortages and rising labor costs

The agricultural sector faces significant workforce challenges, with the American Farm Bureau Federation estimating approximately 2.5 million farm jobs need to be filled annually in the United States alone. Additionally, aging farming populations and the reluctance of younger generations to engage in manual agricultural work have intensified the demand for advanced agricultural equipment that reduces dependence on human labor. Furthermore, autonomous harvesters address these labor gaps by ensuring timely and efficient harvesting operations while operating continuously without breaks, enhancing operational efficiency while reducing long-term labor costs.

Threat:

High initial capital investment and operational costs

The substantial upfront investment required for purchasing and implementing autonomous harvesters creates financial barriers for small and medium-sized farms with limited budgets. These costs encompass not only the purchase price of machinery but also expenses related to installation, setup, integration with existing farm operations, and ongoing maintenance requirements. Furthermore, the advanced technological components, including AI systems, sensors, GPS equipment, and machine learning capabilities, contribute to elevated operational expenses that many farming operations find challenging to justify.

Covid-19 Impact:

The COVID-19 pandemic significantly impacted the autonomous harvester market through supply chain disruptions and temporary manufacturing delays that affected equipment availability. However, the crisis also accelerated adoption of automated farming solutions as labor shortages intensified due to travel restrictions and health concerns. Furthermore, the pandemic highlighted the importance of reducing human dependency in agricultural operations, driving increased interest in autonomous harvesting technologies among farmers seeking operational continuity during uncertain times.

The diesel-powered segment is expected to be the largest during the forecast period

The diesel-powered segment is expected to account for the largest market share during the forecast period due to established infrastructure and proven reliability in agricultural applications. Diesel engines provide superior fuel efficiency compared to gasoline alternatives, resulting in significant cost savings for farmers, particularly in regions with elevated fuel prices. Moreover, the well-developed diesel fuel infrastructure across agricultural regions ensures consistent availability and accessibility for farming operations worldwide. Additionally, recent technological advancements in diesel engine technology, including common rail direct injection systems, have improved fuel efficiency while meeting stringent emission regulations, making them the preferred choice for those seeking reliable and cost-effective solutions.

The fruits & vegetables segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the fruits & vegetables segment is predicted to witness the highest growth rate due to increasing demand for precision in harvesting delicate crops. These specialized crops require careful handling to minimize damage and maintain quality, driving the need for autonomous harvesters equipped with advanced sensors and algorithms designed specifically for gentle harvesting operations. Furthermore, the escalating global demand for fresh produce, coupled with the labor-intensive nature of fruit and vegetable harvesting, creates substantial opportunities for automation solutions. Additionally, real-time data analytics capabilities enable these machines to navigate complex orchard layouts and adapt to varying harvesting conditions, ensuring optimal efficiency and minimal crop loss.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by strong investment in research and development, favorable government incentives, and the presence of leading agricultural technology manufacturers. The United States holds the majority of the North American market share, supported by large-scale farming operations and widespread adoption of precision agriculture technologies. Furthermore, the region benefits from advanced infrastructure supporting mechanized farming, with over 70% of large farms utilizing self-propelled combine harvesters, according to the American Farm Bureau Federation. Moreover, the push toward fully automatic systems, combined with rapid advancements in artificial intelligence and machine learning technologies, continues to drive market expansion across the region.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapid modernization of farming practices and increasing food security concerns. Countries including China, India, and Japan are actively integrating artificial intelligence and IoT technologies into agricultural machinery while addressing significant labor shortages in rural areas. Furthermore, government initiatives promoting precision farming through subsidies and mechanization programs are accelerating the adoption of autonomous harvesting equipment across the region. Additionally, the region's large agricultural geography and fierce drive toward modernization, particularly in India, China, and Indonesia, fuel market expansion throughout the Asia Pacific region.

Key players in the market

Some of the key players in Autonomous Harvester Market include John Deere (Deere & Company), CNH Industrial, AGCO Corporation, Kubota Corporation, CLAAS KGaA mbH, Yanmar Co., Ltd., Mahindra & Mahindra Ltd., SDF Group, Iseki & Co., Ltd., Harvest CROO Robotics, Naio Technologies, Agrobot, Harvest Automation, Inc., Eos Crop Automation, Autonomous Solutions, Inc. (ASI), AgEagle Aerial Systems Inc., Raven Industries, Inc., and Solinftec.

Key Developments:

In March 2025, Kubota Corporation is scheduled to exhibit the "Type: V" and "Type: S" concept models of its versatile platform robots for the future at the Future City pavilion, which Kubota supports as a platinum partner. The Type: V model will be making its world debut at that time.

In January 2025, John Deere revealed several new autonomous machines during a press conference at CES 2025 to support customers in agriculture, construction, and commercial landscaping. Building on Deere's autonomous technology first revealed at CES 2022, the company's second-generation autonomy kit combines advanced computer vision, AI, and cameras to help the machines navigate their environments.

In January 2024, Yanmar Agribusiness Co., Ltd. (Yanmar AG), a subsidiary of Yanmar Holdings, has revealed its e-X1 concept, an electric drive compact electric agricultural machine designed to achieve zero emissions in agriculture.

Product Types Covered:

  • Combine Harvesters
  • Forage Harvesters
  • Turf Harvesters
  • Fruit Harvesters
  • Sugarcane Harvesters
  • Potato Harvesters
  • Vegetable Harvesters
  • Other Harvesters

Level of Automations:

  • Semi-Autonomous Harvesters (Driver-Assisted)
  • Fully Autonomous Harvesters (Driverless)

Propulsion Types Covered:

  • Diesel-Powered
  • Electric
  • Hybrid

Site of Operations Covered:

  • Open-Field Operations
  • Controlled Environment Agriculture (CEA)

Crop Types Covered:

  • Grains & Cereals
  • Fruits & Vegetables
  • Cotton
  • Sugarcane
  • Other Crop Types

Technologies Covered:

  • GPS & GNSS Technology
  • LiDAR & Radar Sensors
  • Computer Vision & Camera Systems
  • Artificial Intelligence & Machine Learning
  • Internet of Things (IoT)
  • Edge Computing
  • Cloud Connectivity & Telematics

End Users Covered:

  • Large Scale Farms
  • Medium Scale Farms
  • Small Scale Farms
  • Agricultural Cooperatives
  • Contract Farming Services

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
  • 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

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Product Analysis
  • 3.7 Technology Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Autonomous Harvester Market, By Product Type

  • 5.1 Introduction
  • 5.2 Combine Harvesters
  • 5.3 Forage Harvesters
  • 5.4 Turf Harvesters
  • 5.5 Fruit Harvesters
  • 5.6 Sugarcane Harvesters
  • 5.7 Potato Harvesters
  • 5.8 Vegetable Harvesters
  • 5.9 Other Harvesters

6 Global Autonomous Harvester Market, By Level of Automation

  • 6.1 Introduction
  • 6.2 Semi-Autonomous Harvesters (Driver-Assisted)
  • 6.3 Fully Autonomous Harvesters (Driverless)

7 Global Autonomous Harvester Market, By Propulsion Type

  • 7.1 Introduction
  • 7.2 Diesel-Powered
  • 7.3 Electric
  • 7.4 Hybrid

8 Global Autonomous Harvester Market, By Site of Operation

  • 8.1 Introduction
  • 8.2 Open-Field Operations
  • 8.3 Controlled Environment Agriculture (CEA)
    • 8.3.1 Greenhouses
    • 8.3.2 Indoor Farms

9 Global Autonomous Harvester Market, By Crop Type

  • 9.1 Introduction
  • 9.2 Grains & Cereals
  • 9.3 Fruits & Vegetables
  • 9.4 Cotton
  • 9.5 Sugarcane
  • 9.6 Other Crop Types

10 Global Autonomous Harvester Market, By Technology

  • 10.1 Introduction
  • 10.2 GPS & GNSS Technology
  • 10.3 LiDAR & Radar Sensors
  • 10.4 Computer Vision & Camera Systems
  • 10.5 Artificial Intelligence & Machine Learning
  • 10.6 Internet of Things (IoT)
  • 10.7 Edge Computing
  • 10.8 Cloud Connectivity & Telematics

11 Global Autonomous Harvester Market, By End User

  • 11.1 Introduction
  • 11.2 Large Scale Farms
  • 11.3 Medium Scale Farms
  • 11.4 Small Scale Farms
  • 11.5 Agricultural Cooperatives
  • 11.6 Contract Farming Services

12 Global Autonomous Harvester Market, By Geography

  • 12.1 Introduction
  • 12.2 North America
    • 12.2.1 US
    • 12.2.2 Canada
    • 12.2.3 Mexico
  • 12.3 Europe
    • 12.3.1 Germany
    • 12.3.2 UK
    • 12.3.3 Italy
    • 12.3.4 France
    • 12.3.5 Spain
    • 12.3.6 Rest of Europe
  • 12.4 Asia Pacific
    • 12.4.1 Japan
    • 12.4.2 China
    • 12.4.3 India
    • 12.4.4 Australia
    • 12.4.5 New Zealand
    • 12.4.6 South Korea
    • 12.4.7 Rest of Asia Pacific
  • 12.5 South America
    • 12.5.1 Argentina
    • 12.5.2 Brazil
    • 12.5.3 Chile
    • 12.5.4 Rest of South America
  • 12.6 Middle East & Africa
    • 12.6.1 Saudi Arabia
    • 12.6.2 UAE
    • 12.6.3 Qatar
    • 12.6.4 South Africa
    • 12.6.5 Rest of Middle East & Africa

13 Key Developments

  • 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 13.2 Acquisitions & Mergers
  • 13.3 New Product Launch
  • 13.4 Expansions
  • 13.5 Other Key Strategies

14 Company Profiling

  • 14.1 John Deere (Deere & Company)
  • 14.2 CNH Industrial
  • 14.3 AGCO Corporation
  • 14.4 Kubota Corporation
  • 14.5 CLAAS KGaA mbH
  • 14.6 Yanmar Co., Ltd.
  • 14.7 Mahindra & Mahindra Ltd.
  • 14.8 SDF Group
  • 14.9 Iseki & Co., Ltd.
  • 14.10 Harvest CROO Robotics
  • 14.11 Naio Technologies
  • 14.12 Agrobot
  • 14.13 Harvest Automation, Inc.
  • 14.14 Eos Crop Automation
  • 14.15 Autonomous Solutions, Inc. (ASI)
  • 14.16 AgEagle Aerial Systems Inc.
  • 14.17 Raven Industries, Inc.
  • 14.18 Solinftec

List of Tables

  • Table 1 Global Autonomous Harvester Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Autonomous Harvester Market Outlook, By Product Type (2024-2032) ($MN)
  • Table 3 Global Autonomous Harvester Market Outlook, By Combine Harvesters (2024-2032) ($MN)
  • Table 4 Global Autonomous Harvester Market Outlook, By Forage Harvesters (2024-2032) ($MN)
  • Table 5 Global Autonomous Harvester Market Outlook, By Turf Harvesters (2024-2032) ($MN)
  • Table 6 Global Autonomous Harvester Market Outlook, By Fruit Harvesters (2024-2032) ($MN)
  • Table 7 Global Autonomous Harvester Market Outlook, By Sugarcane Harvesters (2024-2032) ($MN)
  • Table 8 Global Autonomous Harvester Market Outlook, By Potato Harvesters (2024-2032) ($MN)
  • Table 9 Global Autonomous Harvester Market Outlook, By Vegetable Harvesters (2024-2032) ($MN)
  • Table 10 Global Autonomous Harvester Market Outlook, By Other Harvesters (2024-2032) ($MN)
  • Table 11 Global Autonomous Harvester Market Outlook, By Level of Automation (2024-2032) ($MN)
  • Table 12 Global Autonomous Harvester Market Outlook, By Semi-Autonomous Harvesters (Driver-Assisted) (2024-2032) ($MN)
  • Table 13 Global Autonomous Harvester Market Outlook, By Fully Autonomous Harvesters (Driverless) (2024-2032) ($MN)
  • Table 14 Global Autonomous Harvester Market Outlook, By Propulsion Type (2024-2032) ($MN)
  • Table 15 Global Autonomous Harvester Market Outlook, By Diesel-Powered (2024-2032) ($MN)
  • Table 16 Global Autonomous Harvester Market Outlook, By Electric (2024-2032) ($MN)
  • Table 17 Global Autonomous Harvester Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 18 Global Autonomous Harvester Market Outlook, By Site of Operation (2024-2032) ($MN)
  • Table 19 Global Autonomous Harvester Market Outlook, By Open-Field Operations (2024-2032) ($MN)
  • Table 20 Global Autonomous Harvester Market Outlook, By Controlled Environment Agriculture (CEA) (2024-2032) ($MN)
  • Table 21 Global Autonomous Harvester Market Outlook, By Greenhouses (2024-2032) ($MN)
  • Table 22 Global Autonomous Harvester Market Outlook, By Indoor Farms (2024-2032) ($MN)
  • Table 23 Global Autonomous Harvester Market Outlook, By Crop Type (2024-2032) ($MN)
  • Table 24 Global Autonomous Harvester Market Outlook, By Grains & Cereals (2024-2032) ($MN)
  • Table 25 Global Autonomous Harvester Market Outlook, By Fruits & Vegetables (2024-2032) ($MN)
  • Table 26 Global Autonomous Harvester Market Outlook, By Cotton (2024-2032) ($MN)
  • Table 27 Global Autonomous Harvester Market Outlook, By Sugarcane (2024-2032) ($MN)
  • Table 28 Global Autonomous Harvester Market Outlook, By Other Crop Types (2024-2032) ($MN)
  • Table 29 Global Autonomous Harvester Market Outlook, By Technology (2024-2032) ($MN)
  • Table 30 Global Autonomous Harvester Market Outlook, By GPS & GNSS Technology (2024-2032) ($MN)
  • Table 31 Global Autonomous Harvester Market Outlook, By LiDAR & Radar Sensors (2024-2032) ($MN)
  • Table 32 Global Autonomous Harvester Market Outlook, By Computer Vision & Camera Systems (2024-2032) ($MN)
  • Table 33 Global Autonomous Harvester Market Outlook, By Artificial Intelligence & Machine Learning (2024-2032) ($MN)
  • Table 34 Global Autonomous Harvester Market Outlook, By Internet of Things (IoT) (2024-2032) ($MN)
  • Table 35 Global Autonomous Harvester Market Outlook, By Edge Computing (2024-2032) ($MN)
  • Table 36 Global Autonomous Harvester Market Outlook, By Cloud Connectivity & Telematics (2024-2032) ($MN)
  • Table 37 Global Autonomous Harvester Market Outlook, By End User (2024-2032) ($MN)
  • Table 38 Global Autonomous Harvester Market Outlook, By Large Scale Farms (2024-2032) ($MN)
  • Table 39 Global Autonomous Harvester Market Outlook, By Medium Scale Farms (2024-2032) ($MN)
  • Table 40 Global Autonomous Harvester Market Outlook, By Small Scale Farms (2024-2032) ($MN)
  • Table 41 Global Autonomous Harvester Market Outlook, By Agricultural Cooperatives (2024-2032) ($MN)
  • Table 42 Global Autonomous Harvester Market Outlook, By Contract Farming Services (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.