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市场调查报告书
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1766143

全球自动收割机市场：预测至 2032 年－按产品类型、自动化程度、推进方式、运作地点、作物类型、技术、最终使用者和地区进行分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球自动收割机市场预计在 2025 年达到 19 亿美元，到 2032 年将达到 44 亿美元，预测期内的复合年增长率为 12.8%。

自动收割机是一种自走式农业机械，旨在以最少的人工干预完成收割任务。这些机器利用GPS、感测器、电脑视觉和人工智慧导航系统等先进技术，能够有效率地辨识、收集和加工作物。这提高了生产力，减少了对劳动力的依赖，并确保了农业作业的精准性。自动收割机在大型农场尤其受到重视，因为高效和及时的收割对于获得最佳产量至关重要。

根据联合国估计，到 2022 年 11 月中旬，世界人口将达到 80 亿，而 1950 年预计为 25 亿。

精密农业的采用日益增多

精密农业技术使农民能够透过数据主导的决策、即时监测和自动化流程来优化作物产量，从而提高营运效率。自动收割机与农场管理系统无缝集成，并提供先进的感测器、机器学习功能和即时数据分析，从而提高农业作业的准确性和生产力。此外，这些系统还能增强作物健康监测，优化收穫计划，简化资源管理，使其成为现代农业企业不可或缺的工具。

缺乏操作先进系统的技术纯熟劳工

缺乏操作先进自动收割系统的技术纯熟劳工，为农业企业带来了营运挑战。这些先进的机器依赖复杂的技术，包括人工智慧、机器学习、感测器和GPS系统，需要专业知识和技术专长才能有效地操作和维护它们。此外，自动收割机的复杂性要求对农业工人进行持续的培训和技能提升，而许多农业企业由于资源和技术教育计画有限而难以提供这些培训和技能。这种技能缺口尤其影响那些无法聘请专业技术人员的中小型农业企业。

劳动力短缺和人事费用上升

农业部门面临劳动力短缺的重大挑战，美国农场联合会估计，光是美国每年就需要僱用约250万名农场工人。此外，农业人口老化以及年轻一代不愿从事体力劳动，也增加了对先进农业机械的需求，这些机械可以减少对劳动力的依赖。此外，自动化收割机可以连续不间断地作业，确保及时且有效率的收割作业，从而解决劳动力短缺问题，提高作业效率，并降低长期人事费用。

初期资本投入及营运成本高

购买和部署自动收割机所需的巨额前期投资，对于预算有限的中小型农场来说，构成了经济障碍。这些成本不仅包括机械的购买价格，还包括安装、设置、与现有农场运营模式的整合以及持续维护相关的费用。此外，人工智慧系统、感测器、GPS 设备和机器学习等先进技术组件也导致营运成本上升，许多农业经营者难以承受。

COVID-19的影响：

新冠疫情对自动收割机市场造成了重大衝击，供应链中断和短暂的生产延误影响了设备的供应。然而，由于出行限制和健康担忧加剧了劳动力短缺，这场危机也加速了自动化农业解决方案的采用。此外，疫情凸显了减少农业生产中对人力的依赖的重要性，促使那些在不确定时期寻求持续经营的农民对自动收割技术产生了浓厚的兴趣。

预计柴油引擎市场在预测期内将占据最大份额

凭藉完善的基础设施和在农业应用中久经考验的可靠性，柴油引擎预计将在预测期内占据最大的市场占有率。与汽油引擎相比，柴油引擎具有更高的燃油效率，这为农民节省了大量成本，尤其是在燃油价格高涨的地区。此外，全部区域完善的柴油基础设施确保了全球农业作业的稳定供应和便利性。此外，柴油引擎技术的最新进展，例如共轨缸内直喷系统，在满足严格的排放法规的同时提高了燃油经济性，使其成为寻求可靠且经济高效解决方案的农民的首选。

预计水果和蔬菜板块在预测期内将达到最高复合年增长率

在预测期内，由于对精细作物精准采收的需求日益增长，预计水果和蔬菜领域将呈现最高成长率。这些特殊作物需要小心处理，以最大限度地减少损害并保持其品质，这推动了对配备先进感测器和专为轻柔采收作业设计的演算法的自动采收机的需求。此外，全球对新鲜农产品的需求不断增长，再加上水果和蔬菜采收的劳动密集性质，为自动化解决方案创造了巨大的机会。此外，即时数据分析功能使这些机器能够导航复杂的果园布局并适应不断变化的采收条件，从而确保最佳效率并将作物损失降至最低。

占比最大的地区：

由于对研发的大力投资、有利的政府激励措施以及主要农业技术製造商的存在，预计北美将在预测期内占据最大的市场占有率。受大规模农业经营和精密农业农业市场占有率的广泛采用的推动，美国占据了北美大部分市场份额。此外，该地区受益于支持机械化农业的先进基础设施，根据美国农场联合会的数据，超过 70% 的大型农场使用自走式联合收割机。此外，全自动系统的推动，加上人工智慧和机器学习技术的快速发展，继续推动全部区域的市场扩张。

复合年增长率最高的地区：

在预测期内，由于农业方法的快速现代化和对粮食安全的日益担忧，预计亚太地区将呈现最高的复合年增长率。包括中国、印度和日本在内的国家正积极将人工智慧和物联网技术应用于农业机械，同时解决农村地区严重的劳动力短缺问题。此外，政府透过补贴和机械化计画推动精密农业的倡议，正在加速全部区域自动收割机的普及。此外，该地区广阔的农业地理和对现代化的强烈追求，尤其是在印度、中国和印尼，正在推动全部区域亚太地区的市场扩张。

自动化水平

  • 半自动收割机（驾驶员辅助）
  • 全自动收割机（无人驾驶）

免费客製化服务

本报告的所有订阅者均可享有以下免费自订选项之一：

  • 公司简介
    • 对其他公司（最多 3 家）进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家市场进行估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第 2 章 简介

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 主要研究资料
    • 二手研究资讯来源
    • 先决条件

第三章市场走势分析

  • 介绍
  • 驱动程式
  • 限制因素
  • 市场机会
  • 威胁
  • 产品分析
  • 技术分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 企业之间的竞争

第五章全球自动收割机市场（依产品类型）

  • 结合
  • 青贮收割机
  • 割草机
  • 水果采摘机
  • 甘蔗收割机
  • 马铃薯收割机
  • 蔬菜收穫机
  • 其他收割机

6. 全球自动收割机市场（依自动化程度）

  • 半自动收割机（辅助驾驶）
  • 全自动收割机（无人驾驶）

7. 全球自动收割机市场（依推进型）

  • 柴油引擎
  • 电
  • 杂交种

8. 全球自动收割机市场（按运作）

  • 农事
  • 受控环境农业（CEA）
    • 温室
    • 室内农场

第九章。全球自动收割机市场（按作物类型）

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

第十章。全球自动收割机市场（按技术）

  • GPS/GNSS技术
  • 光达/雷达感测器
  • 电脑视觉摄影系统
  • 人工智慧和机器学习
  • 物联网 (IoT)
  • 边缘运算
  • 云端连线和远端资讯处理

第 11 章全球自动收割机市场（按最终用户）

  • 大型农场
  • 中型农场
  • 小型农场
  • 农业合作社
  • 合约农业服务

第 12 章全球自动收割机市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十三章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併与收购（M&A）
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十四章 公司简介

  • 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.