表紙
市場調查報告書
商品編碼
1053882

農業機器人市場 2022-2032年

Agricultural Robotics Market 2022-2032

出版日期: | 出版商: IDTechEx Ltd. | 英文 | 商品交期: 最快1-2個工作天內

價格
  • 全貌
  • 簡介
  • 目錄
簡介

標題
農業機器人市場 2022-2032年
對農業機器人行業的技術和市場評估,包括除草機器人、播種機器人、自主拖拉機、機器人工具載體、機器人收割機、農業無人機和擠奶機器人。

"到 2032 年,全球農業機器人市場將達到 78.8 億美元。"

隨著農業勞動力變得越來越昂貴和稀缺,COVID-19 危機加劇了這種情況,人們越來越關注機器人作為農業生產的關鍵組成部分。

IDTechEx 的這份報告對不斷增長的農業機器人市場進行了技術和商業分析,同時考慮了支持該行業的關鍵應用領域和使能技術。該報告還提供了農業機器人行業未來十年應用和區域市場預測。

作為解決全球農業面臨的可持續性和勞工問題的潛在解決方案,農業機器人越來越引起人們的興趣。近年來,農業勞動力變得越來越昂貴和稀缺,尤其是在 COVID-19 大流行之後邊境關閉和工人旅行限制之後,進一步擠壓了農民的利潤並威脅到世界各地的糧食安全。

自動化可以幫助緩解這種情況。在過去十年中,機器人技術和人工智能 (AI) 的進步使農業機器人的使用成為越來越可行的選擇。在世界各地,許多初創企業和成熟公司都在努力開發機器人解決方案,以解決包括除草、播種和收割在內的多項農業任務。

IDTechEx 的一份新報告《2022-2032 年農業機器人》全面概述了農業機器人,重點關注農業機器人的關鍵應用領域、支撐該行業增長的使能技術以及影響農業機器人的市場因素。將塑造該領域的未來。該報告還提供了農業機器人行業未來十年市場預測,按區域市場份額和應用領域細分,預測到 2032 年,全球農業機器人市場價值將達到 67 億美元。

IDTechEx 報告將全球農業機器人行業劃分為八個關鍵應用領域:除草機器人、播種機器人、自主拖拉機、自主執行載體和平台機器人、機器人收割、農業無人機、擠奶機器人以及農業機器人的其他應用。考慮的關鍵使能技術包括 RTK-GPS、LiDAR、人工智能 (AI)、高光譜成像、末端執行器技術和精密噴塗技術。

本報告中回答的關鍵問題:

  • 全球農業面臨哪些可持續發展和勞工問題?
  • 什麼是農業機器人?
  • 農業機器人的主要應用領域有哪些?
  • 農業機器人行業面臨的主要技術障礙是什麼?
  • 誰是該領域的主要參與者?
  • 開發農業機器人的主要商業模式考慮因素是什麼?
  • 未來十年,全球農業機器人市場將如何發展?

農業機器人:新興技術推動的不斷發展的行業

農場的日常運營涉及一系列重複、耗時且危險的任務,這些任務可能非常適合使用機器人技術實現自動化。在其中一些任務中,自動化已經很普遍。例如,機器人擠奶已經是一個價值數十億美元的產業,歐洲有很大比例的農場使用機器人擠奶。農業無人機也開始在成像和噴灑方面得到廣泛應用,儘管法規繼續限制它們在世界大部分地區的使用,並且任務的自主性仍然受到一定限制。儘管如此,預計農業無人機市場將在未來十年的大部分時間裡呈現強勁增長。

其他應用仍在不斷湧現。用於除草和播種等任務的田間機器人正在進入商業化的早期階段。與通常在室內操作的固定機器人擠奶機器人相比,開發自主現場機器人提出了一些技術挑戰,這些挑戰在歷史上進展有限。農業環境通常具有不可預測的地形、未知的障礙物以及一系列可能影響自主導航和操作並限制可靠性的天氣條件。此外,農業地區通常位於偏遠農村地區,那裡的連通性和維修和維護服務可能受到限制。

儘管如此,人工智能 (AI)、計算機視覺和定位技術的進步和進步使現場機器人比以往任何時候都更接近商業化。 Naïo Technologies、ecoRobotix 和 TerraClear 等初創公司已開始將機器人商業化,用於各種農業任務,而約翰迪爾、愛科和久保田等主要設備供應商已經開發了自動拖拉機概念。 Fendt MARS 項目讓人們對農業機器人的未來有了一瞥,該項目使用一群小型自主機器人來執行通常由載人拖拉機執行的任務,該公司利用該項目的成果開發其 Xaver 系列農業機器人。展望未來,Octinion、Harvest CROO 和 FFRobotics 等公司正在開髮用於收穫新鮮水果的機器人,這目前涉及成本高且難以採購的勞動力,但使用機器人很難替代,需要仔細平衡計算機視覺、準確定位和軟握技術。

農業機器人的興起導致新的價值鏈出現。

農業機器人行業的發展也引發了圍繞最佳商業模式的爭論,尤其是機器人即服務 (RaaS) 與傳統設備銷售之間的爭論。在機器人即服務模式中,與傳統的機器/設備銷售相比,機器人與受過培訓的操作員一起由農場僱用。這有助於降低農民的運營風險,避免在部署前支付高昂的前期成本或開發技術專業知識。但是,它還需要一支訓練有素的操作員團隊,這可能會阻止開發人員在新的地區進行操作並限制可擴展性。關於數據所有權以及數據是否屬於農民、數據收集者、技術提供者或土地所有者的問題也存在疑問。相關法規尚未跟上技術發展的步伐,這是農業機器人產業未來的關鍵不確定性。

未來幾年的發展將在農業機器人行業的發展中發揮關鍵作用。 IDTechEx 的一份新報告《農業機器人 2022-2032》探討了所有這些問題,分析了將塑造圍繞農業機器人的新興產業未來的技術和市場因素,提供按地區和應用細分的十年市場預測地區。

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目錄

1。執行摘要

  • 1.1.1.什麼是農業機器人?
  • 1.1.2.農業機器人的當前用途
  • 1.1.3.農業機器人的潛在用途
  • 1.1.4.農業數字化歷來緩慢
  • 1.1.5。這種情況開始發生變化:為農業開發數字和機器人解決方案的公司
  • 1.1.6。農業機器人的現狀
  • 1.1.7.農業機器人:驅動因素和限制
  • 1.1.8。精準農業趨勢
  • 1.1.9。農業機器人的應用
  • 1.1.10。按技術成熟度劃分的應用領域
  • 1.1.11。技術向自主、超精密除草方向發展
  • 1.1.12。用於精準播種的可變速率技術
  • 1.1.13。無人駕駛自動大型拖拉機的技術進步
  • 1.1.14。小型自主機器人與拖拉機
  • 1.1.15。無人機變得越來越自主
  • 1.1.16。無人機適合農場的什麼地方?
  • 1.1.17。機器人擠奶:更廣泛的農業機器人產業的藍圖?
  • 1.1.18。哪些作物領域將首先看到農業機器人?
  • 1.1.19。農業機器人和精準農業可能導致新的價值鏈出現
  • 1.1.20。農業機器人的發展仍然緩慢
  • 1.1.21。農業機器人,按機器人類別的市場預測
  • 1.1.22。農業機器人,按地區劃分的市場預測

2。簡介

  • 2.1. 21 世紀農業面臨的挑戰:生產力和勞動力問題
    • 2.1.1. 21世紀農業面臨重大挑戰
    • 2.1.2.農業就業正在下降
    • 2.1.3.隨著財富的增加,農業就業減少,但農業生產力提高
    • 2.1.4.農業勞動力短缺
    • 2.1.5。農業勞動力成本正在上升
    • 2.1.6.農產品價格下跌正在收緊利潤
    • 2.1.7.農業自動化是解決方案的一部分嗎?
  • 2.2. 21世紀農業面臨的挑戰:農用化學品
    • 2.2.1.化肥對環境的影響
    • 2.2.2.全球農藥使用
    • 2.2.3.全球農藥使用趨勢
    • 2.2.4.農藥法規越來越嚴格
    • 2.2.5。農藥對環境的影響
    • 2.2.6.農用化學品的開發成本越來越高
    • 2.2.7.綜述訴訟:對除草劑的潛在打擊
    • 2.2.8.農藥抗性
    • 2.2.9.精準農業是解決方案的一部分嗎?
  • 2.2.10。精準農業趨勢
  • 2.3.農業機器人
    • 2.3.1.什麼是農業機器人?
    • 2.3.2.農業機器人的當前用途
    • 2.3.3.農業機器人的潛在用途
    • 2.3.4.農業數字化歷來緩慢
    • 2.3.5。這種情況開始發生變化:為農業開發數字和機器人解決方案的公司
    • 2.3.6.機器人技術:替代或補充人類勞動力?
    • 2.3.7.農業機器人的現狀
    • 2.3.8. COVID-19 對農業的影響
    • 2.3.9.開發農業機器人:比其他行業更具挑戰性?
    • 2.3.10。農業機器人:驅動因素和限制
    • 2.3.11。自治水平
    • 2.3.12。完全自治可能嗎?
    • 2.3.13。自主傳感器技術
    • 2.3.14。衛星定位
    • 2.3.15。電動與非電動農業機器人
    • 2.3.16。平均農場有多大?

3。農業機器人:關鍵應用領域

  • 3.1.1.農業機器人的應用
  • 3.1.2.按技術成熟度劃分的應用領域
  • 3.2.雜草和害蟲控制
    • 3.2.1.大多數商業田間機器人用於除草
    • 3.2.2.從人工、廣播噴灑到自主精準除草
    • 3.2.3.技術向自主、超精密除草方向發展
    • 3.2.4. Naïo Technologies 的 Oz
    • 3.2.5。 Naïo Technologies 的 Dino
    • 3.2.6. Vitirover 的自動除草機器人
    • 3.2.7. Carre的Anatis
    • 3.2.8.機器人除草的挑戰
    • 3.2.9.不同除草方法的比較
    • 3.2.10。 "智能除草" 與傳統除草
    • 3.2.11。 Ekobot 的 GEN-2
    • 3.2.12。 Odd.Bot 的除草機
    • 3.2.13。 Roush 和 FarmWise 的 Titan FT-35
    • 3.2.14. Pixelfarming Robotics 的機器人一號
    • 3.2.15。精密噴塗
    • 3.2.16. "綠色對綠色" 與 "綠色對棕色"
    • 3.2.17.約翰迪爾收購藍河科技
    • 3.2.18.藍河科技 (John Deere): "看見並噴灑"
    • 3.2.19. ecoRobotix 的 Avo
    • 3.2.20。 Jacto 的 Arbus 4000 JAV
    • 3.2.21。 Kilter 的 AX-1
    • 3.2.22。除草的新方法
    • 3.2.23。小機器人公司的迪克
    • 3.2.24。機器人害蟲防治:超越雜草
    • 3.2.25。 Agrobot 的吸蟲器
  • 3.3.機器人播種
    • 3.3.1.自動播種
    • 3.3.2.用於精準播種的可變速率技術
    • 3.3.3. FarmDroid 的 FD20
    • 3.3.4. FarmBot的創世紀
  • 3.4.全自動拖拉機
    • 3.4.1.小機器人還是大拖拉機?
    • 3.4.2.無人駕駛自動大型拖拉機的技術進步
    • 3.4.3.大型拖拉機的拖拉機引導和自動轉向技術
    • 3.4.4.拖拉機自動轉向 - 邁向自主的第一步
    • 3.4.5。半自動 "跟我來" 拖拉機
    • 3.4.6. H2Trac 的 EOX-175
    • 3.4.7.全自動無人駕駛拖拉機
    • 3.4.8.主要拖拉機公司開發的自動拖拉機概念
    • 3.4.9.全自動拖拉機何時準備就緒?
    • 3.4.10。君主拖拉機
    • 3.4.11。 Farmertronics 的 eTrac
    • 3.4.12。 AgXeed 的 AgBot
    • 3.4.13。現有拖拉機的全自動化
  • 3.5.自動機具載體和平台機器人
    • 3.5.1.小型自主機器人與拖拉機
    • 3.5.2. Directed Machines 的土地護理機器人
    • 3.5.3. Korechi 的 RoamIO
    • 3.5.4. SwarmFarm Robotics 的 SwarmBot 5
    • 3.5.5。定制工具還是標準工具?
    • 3.5.6.烏鴉工業的點
    • 3.5.7. Agrointelli 的 Robotti 150D
    • 3.5.8.跨行葡萄園機器人
    • 3.5.9. VitiBot 的 Bakus
    • 3.5.10。泰德由 Naïo Technologies
    • 3.5.11。 SITIA 的 Trektor
    • 3.5.12。跨行葡萄園機器人的比較
    • 3.5.13。湖北三思智能科技有限公司的農用噴霧器。
    • 3.5.14。對未來的憧憬?芬特火星項目
  • 3.6.機器人新鮮水果和蔬菜收穫
    • 3.6.1.大田作物和非新鮮水果的收穫在很大程度上實現了機械化
    • 3.6.2.新鮮水果採摘仍然主要依靠人工
    • 3.6.3.草莓和蘋果:最受歡迎的目標
    • 3.6.4.機器人收割:蘋果
    • 3.6.5。 FFRobotics 的 FFRobot 蘋果收割機
    • 3.6.6.機器人收割:草莓
    • 3.6.7.草莓收穫機器人開發者的比較
    • 3.6.8.先進開發中的草莓採摘機器人
    • 3.6.9. Harvest CROO Robotics 的 Harvester B7
    • 3.6.10。 Octinion的魯比恩
    • 3.6.11。機器人收割:蘆筍
    • 3.6.12。機器人蘆筍收穫項目
    • 3.6.13。 Cerescon 的 Sparter
    • 3.6.14。其他作物的機器人收割正在開發中
    • 3.6.15。開發水果採摘機器人的挑戰
  • 3.7.農業無人機
    • 3.7.1.無人機:應用管道
    • 3.7.2.農業無人機
    • 3.7.3.商用農業無人機
    • 3.7.4.農業無人機:關鍵考慮因素
    • 3.7.5。農業中的航空成像
    • 3.7.6.無人機 vs. 衛星 vs. 飛機
    • 3.7.7.無人機噴灑在哪裡獲得監管批准?
    • 3.7.8.市售噴灑無人機
    • 3.7.9.無人機變得越來越自主
    • 3.7.10。農業無人機:公司景觀
    • 3.7.11。農業無人機的潛在軟件機會
    • 3.7.12。無人機適合農場的什麼地方?
    • 3.7.13。 Tevel Aerobotics Technologies 的水果採摘無人機
    • 3.7.14。 HayBeeSee 的 CropHopper
  • 3.8.擠奶機器人和其他機器人奶牛養殖
    • 3.8.1.奶牛場規模的全球趨勢和平均值
    • 3.8.2.全球奶牛數量和地區分佈
    • 3.8.3.機器人(自動)擠奶
    • 3.8.4.機器人擠奶正變得越來越普遍
    • 3.8.5.機器人擠奶:更廣泛的農業機器人產業的藍圖?
    • 3.8.6.機器人擠奶:優點和缺點
    • 3.8.7.機器人擠奶:關鍵參與者
    • 3.8.8.機器人飼料推動器
  • 3.9.其他應用
    • 3.9.1.彼得羅·裡維的 PothaFacile
    • 3.9.2.小機器人公司的湯姆
    • 3.9.3. TerraClear 的岩石拾取器

4。使能技術

  • 4.1.定位技術:RTK-GPS、LiDAR 等
    • 4.1.1.自主農業機器人的導航
    • 4.1.2.農業環境中的導航
    • 4.1.3.安全定位的挑戰
    • 4.1.4.位置精度與位置完整性
    • 4.1.5。實現安全定位
    • 4.1.6.固定位置公司
    • 4.1.7.綠色文化
    • 4.1.8. GPS作為導航工具
    • 4.1.9. RTK 系統:運營、性能和價值鏈
    • 4.1.10。用於農業的 RTK 系統:價值鏈
    • 4.1.11。 RTK-GPS的挑戰
    • 4.1.12。激光雷達
    • 4.1.13。激光雷達、雷達、攝像頭和超聲波傳感器:比較
    • 4.1.14。飛行時間 (TOF) LiDAR:空間數據分析
    • 4.1.15。市場上或開發中的不同 LiDAR 的性能比較
    • 4.1.16。評估不同激光雷達對農業機器人應用的適用性
  • 4.2.高光譜成像
    • 4.2.1.高光譜成像簡介
    • 4.2.2.獲取高光譜數據立方體的多種方法
    • 4.2.3.線掃瞄高光譜相機設計
    • 4.2.4.快照高光譜成像
    • 4.2.5。高光譜成像照明
    • 4.2.6.高光譜成像作為多光譜成像的發展
    • 4.2.7.高光譜和多光譜成像之間的權衡
    • 4.2.8.高光譜成像與精準農業
    • 4.2.9. UAV(無人機)的高光譜成像
    • 4.2.10。使用高光譜相機進行衛星成像
    • 4.2.11。 Gamaya:用於農業分析的高光譜成像
    • 4.2.12。供應商概述:高光譜成像
  • 4.3.人工智能 (AI)
    • 4.3.1.什麼是人工智能?
    • 4.3.2.關鍵人工智能方法
    • 4.3.3.主要的深度學習 (DL) 方法
    • 4.3.4. DL 使自動圖像識別成為可能
    • 4.3.5。圖像識別 AI 基於卷積神經網絡 (CNN)
    • 4.3.6. CNN 的工作原理:圖像是如何處理的?
    • 4.3.7. CNN 的工作原理:另一個例子
    • 4.3.8.機器學習在農業中的潛在應用
    • 4.3.9.用於雜草識別的人工智能
    • 4.3.10。圖像分析的挑戰
    • 4.3.11。深化神經網絡以提高準確性
    • 4.3.12。深度學習: "足夠準確" 有多準確?
    • 4.3.13。農業機器人案例研究中的人工智能 - ecoRobotix:作物和雜草識別的深度學習
    • 4.3.14。農業機器人案例研究中的人工智能 - ecoRobotix:自主移動
  • 4.4.末端執行器和夾持器技術
    • 4.4.1.水果收穫的末端執行器技術
    • 4.4.2.設計收割末端執行器
    • 4.4.3.用於蘋果收穫的末端執行器
    • 4.4.4.番茄收穫末端執行器
    • 4.4.5。黃瓜收穫的末端執行器
    • 4.4.6.用於辣椒(辣椒)收穫的末端執行器
  • 4.5。精密噴塗技術
    • 4.5.1.什麼是精密噴塗?
    • 4.5.2.噴霧控制方法
    • 4.5.3.脈衝寬度調製 (PWM) 噴塗

5。市場因素

  • 5.1.市場因素和商業模式考慮
    • 5.1.1.哪些作物領域將首先看到農業機器人?
    • 5.1.2.農業機器人和精準農業可能導致新的價值鏈出現
    • 5.1.3.農業機器人的發展仍然緩慢
    • 5.1.4.主要農機設備供應商收入
    • 5.1.5。機器人即服務 (RaaS) 與設備銷售
    • 5.1.6.開發成功的商業模式
    • 5.1.7.農業機器人的投資策略
  • 5.2.農業機器人的主要市場挑戰
    • 5.2.1.農業機器人的成本
    • 5.2.2. IT基礎設施
    • 5.2.3.數字數據的所有權和管理
    • 5.2.4.在農場採用機器人技術

6。預測

  • 6.1.農業機器人,按機器人類別的市場預測
  • 6.2.農業機器人,按機器人類別的市場預測:數據表
  • 6.3.農業機器人,按地區劃分的市場預測
  • 6.4.農業機器人,按地區劃分的市場預測:數據表
  • 6.5。擠奶機器人,按地區劃分的市場預測
  • 6.6.除草機器人和播種機器人,按地區劃分的市場預測
  • 6.7.自主拖拉機和農具搬運機器人,按地區劃分的市場預測
  • 6.8。用於新鮮水果和蔬菜收穫的機器人,按地區劃分的市場預測
  • 6.9。農業無人機,按地區劃分的市場預測
目錄
Product Code: ISBN 9781913899707

Title:
Agricultural Robotics Market 2022-2032
A technological and market evaluation of the agricultural robotics industry including weeding robots, seeding robots, autonomous tractors, robotic implement carriers, robotic harvesting, agricultural drones, and milking robots.

"The global market for agricultural robotics will reach $7.88 billion by 2032."

As agricultural labour becomes increasingly costly and scarce, something exacerbated by the COVID-19 crisis, attention is increasingly turning towards robotics as a key component of agricultural production.

This report from IDTechEx provides a technical and commercial analysis of the growing market for agricultural robotics, considering both the key application areas and enabling technologies underpinning the industry. The report also provides ten-year application-based and regional market forecasts for the future of the agricultural robotics industry.

Agricultural robotics are increasingly attracting interest as a potential solution to the sustainability and labour issues facing global agriculture. In recent years, agricultural labour has steadily become costlier and scarcer, particularly following the border closures and worker travel restrictions in the wake of the COVID-19 pandemic, further squeezing farmers' margins and threatening food security across the world.

Automation could help mitigate this. Over the last decade, advances in robotics technology and artificial intelligence (AI) have made the use of farming robots an increasingly viable option. Across the world, a range of start-ups and established companies are working to develop robotic solutions for a number of agricultural tasks, including weeding, seeding, and harvesting.

Agricultural Robotics 2022-2032, a new report from IDTechEx, provides a comprehensive overview of agricultural robotics, focusing on the key application areas of agricultural robotics, the enabling technologies that are underpinning the growth of the industry, and the market factors that will shape the future of the field. The report also provides a ten-year market forecast for the future of the agricultural robotics industry, broken down by regional market share and by application area, predicting that the global agricultural robotics market will be worth $6.7 billion by 2032.

The IDTechEx report divides the global agricultural robotics industry into eight key application areas: weeding robots, seeding robots, autonomous tractors, autonomous implement carriers and platform robots, robotic harvesting, agricultural drones, milking robots, and other applications of agricultural robots. Key enabling technologies considered include RTK-GPS, LiDAR, artificial intelligence (AI), hyperspectral imaging, end effector technology, and precision spraying technology.

Key questions answered in this report:

  • What are the sustainability and labour issues facing global agriculture?
  • What is agricultural robotics?
  • What are the key application areas of agricultural robotics?
  • What are the main technological hurdles facing the agricultural robotics industry?
  • Who are the main players in the field?
  • What are the key business model considerations in developing agricultural robots?
  • How will the global agricultural robotics market evolve over the next decade?

Agricultural robotics: a growing industry enabled by emerging technologies

The day-to-day operation of a farm involves a range of repetitive, time-consuming, and dangerous tasks that could be well suited to automation using robotics. Automation is already widespread in some of these tasks. Robotic milking, for example, is already a billion-dollar industry with a significant percentage of farms in Europe using a form of robotic milking. Agricultural drones are also beginning to find widespread application in imaging and spraying, although regulations continue to limit their usage across much of the world and autonomy of tasks remains somewhat limited. Nevertheless, the market for agricultural drones is expected to show strong growth over much of the next decade.

Other applications are still emerging. Field robots for tasks such as weeding and seeding are entering the early stages of commercialisation. Compared with milking robots, which are stationary robots that generally operate indoors, developing autonomous field robots presents several technical challenges that have historically limited progress. Agricultural environments often feature unpredictable terrain, unknown obstacles, and a range of weather conditions that can impair autonomous navigation and operation and limit reliability. Additionally, agricultural regions are often in highly rural areas, where connectivity and access to repair and maintenance services can be limited.

Despite this, progress is being made and advances in artificial intelligence (AI), computer vision, and positioning technologies have brought field robots closer than ever to commercialisation. Start-ups such as Naïo Technologies, ecoRobotix, and TerraClear have begun commercialising robots for a diverse range of agricultural tasks, while major equipment providers such as John Deere, AGCO, and Kubota have developed autonomous tractor concepts. The Fendt MARS project provided a glimpse into the future of farm robots, using a swarm of small, autonomous robots to carry out tasks usually performed by manned tractors, with the company using the results of this project to develop its Xaver line of agricultural robots. Looking further into the future, companies such as Octinion, Harvest CROO, and FFRobotics are developing robots for harvesting fresh fruit, something that currently involves costly and difficult-to-source labour but is very difficult to replace using robots, requiring a careful balance of computer vision, accurate positioning, and soft-grip technology.

The rise of agricultural robotics is leading to new value chains emerging.

The growth of the agricultural robotics industry has also led to debate around the best business models, particularly around robotics-as-a-service (RaaS) versus traditional equipment sale. In a robotics-as-a-service model, robots are hired by farms, alongside trained operators, vs. traditional machine/equipment sales. This can help de-risk the operation for farmers, avoiding the need to meet high upfront costs or develop expertise in the technology before deployment. However, it also requires a team of trained operators, which can prevent developers from operating in new geographies and limit scalability. There are also questions around the issue of data ownership and whether data belongs to farmers, data collectors, technology providers, or landowners. Regulations around this have not yet caught up with the pace of technology development and this is a key uncertainty over the future of the agricultural robotics industry.

Developments over the next few years are set to play a pivotal role in the progress of the agricultural robotics industry. Agricultural Robotics 2022-2032, a new report from IDTechEx, explores all of these issues, analysing both the technological and market factors that will shape the future of the emerging industry around farming robots, providing ten-year market forecasts broken down by region and application area.

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TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

  • 1.1.1. What are agricultural robots?
  • 1.1.2. Current uses of agricultural robots
  • 1.1.3. Potential uses of agricultural robots
  • 1.1.4. Agriculture has historically been slow to digitise
  • 1.1.5. This is beginning to change: companies developing digital and robotic solutions for agriculture
  • 1.1.6. The state of agricultural robotics
  • 1.1.7. Agricultural robotics: drivers and restraints
  • 1.1.8. The trend towards precision agriculture
  • 1.1.9. Applications of agricultural robotics
  • 1.1.10. Application areas by technology readiness
  • 1.1.11. Technology progression towards autonomous, ultra precision de-weeding
  • 1.1.12. Variable rate technology for precision seed planting
  • 1.1.13. Technology progression towards driverless autonomous large-sized tractors
  • 1.1.14. Small autonomous robots vs. tractors
  • 1.1.15. Drones are becoming increasingly autonomous
  • 1.1.16. Where do drones fit in on a farm?
  • 1.1.17. Robotic milking: a blueprint for the wider agricultural robotics industry?
  • 1.1.18. Which crop sectors will see agricultural robots first?
  • 1.1.19. Agricultural robotics and precision agriculture could lead to a new value chain emerging
  • 1.1.20. Development in agricultural robotics remains slow
  • 1.1.21. Agricultural robotics, market forecast by robot category
  • 1.1.22. Agricultural robotics, market forecast by region

2. INTRODUCTION

  • 2.1. Challenges facing 21st century agriculture: productivity and labour issues
    • 2.1.1. 21st century agriculture is facing major challenges
    • 2.1.2. Employment in agriculture is declining
    • 2.1.3. As wealth increases, employment in agriculture decreases but agricultural productivity increases
    • 2.1.4. Agricultural labour shortages
    • 2.1.5. Agricultural labour costs are rising
    • 2.1.6. Falling agricultural prices are tightening margins
    • 2.1.7. Is agricultural automation part of the solution?
  • 2.2. Challenges facing 21st century agriculture: agrochemicals
    • 2.2.1. The environmental impact of fertilizers
    • 2.2.2. Global pesticide use
    • 2.2.3. Trends in global pesticide use
    • 2.2.4. Regulations around pesticides are getting harsher
    • 2.2.5. The environmental impact of pesticides
    • 2.2.6. Agrochemicals are getting more expensive to develop
    • 2.2.7. Roundup lawsuits: a potential blow for herbicides
    • 2.2.8. Pesticide resistance
    • 2.2.9. Is a precision agriculture approach part of the solution?
  • 2.2.10. The trend towards precision agriculture
  • 2.3. Agricultural robotics
    • 2.3.1. What are agricultural robots?
    • 2.3.2. Current uses of agricultural robots
    • 2.3.3. Potential uses of agricultural robots
    • 2.3.4. Agriculture has historically been slow to digitise
    • 2.3.5. This is beginning to change: companies developing digital and robotic solutions for agriculture
    • 2.3.6. Robotics: replacing or complementing human labour?
    • 2.3.7. The state of agricultural robotics
    • 2.3.8. The impact of COVID-19 on agriculture
    • 2.3.9. Developing agricultural robots: more challenging than other industries?
    • 2.3.10. Agricultural robotics: drivers and restraints
    • 2.3.11. Levels of autonomy
    • 2.3.12. Is full autonomy possible?
    • 2.3.13. Autonomous sensor technologies
    • 2.3.14. Satellite positioning
    • 2.3.15. Electric vs non-electric agricultural robots
    • 2.3.16. How large is the average farm?

3. AGRICULTURAL ROBOTICS: KEY APPLICATION AREAS

  • 3.1.1. Applications of agricultural robotics
  • 3.1.2. Application areas by technology readiness
  • 3.2. Weed and pest control
    • 3.2.1. Most commercial field robots are used for weeding
    • 3.2.2. From manned, broadcast spraying towards autonomous precision weeding
    • 3.2.3. Technology progression towards autonomous, ultra precision de-weeding
    • 3.2.4. Oz by Naïo Technologies
    • 3.2.5. Dino by Naïo Technologies
    • 3.2.6. Autonomous weeding robots by Vitirover
    • 3.2.7. Anatis by Carré
    • 3.2.8. Challenges in robotic weeding
    • 3.2.9. A comparison of different weeding methods
    • 3.2.10. "Smart weeding" vs. traditional weeding
    • 3.2.11. GEN-2 by Ekobot
    • 3.2.12. Weed Whacker by Odd.Bot
    • 3.2.13. Titan FT-35 by Roush and FarmWise
    • 3.2.14. Robot One by Pixelfarming Robotics
    • 3.2.15. Precision spraying
    • 3.2.16. "Green-on-green" vs. "green-on-brown"
    • 3.2.17. John Deere's acquisition of Blue River Technology
    • 3.2.18. Blue River Technology (John Deere): "See and Spray"
    • 3.2.19. Avo by ecoRobotix
    • 3.2.20. Arbus 4000 JAV by Jacto
    • 3.2.21. AX-1 by Kilter
    • 3.2.22. Novel methods for weed removal
    • 3.2.23. Dick by Small Robot Company
    • 3.2.24. Robotic pest control: beyond weeds
    • 3.2.25. Bug Vacuum by Agrobot
  • 3.3. Robotic seeding
    • 3.3.1. Automating seeding
    • 3.3.2. Variable rate technology for precision seed planting
    • 3.3.3. FD20 by FarmDroid
    • 3.3.4. Genesis by FarmBot
  • 3.4. Fully autonomous tractors
    • 3.4.1. Small robots or big tractors?
    • 3.4.2. Technology progression towards driverless autonomous large-sized tractors
    • 3.4.3. Tractor guidance and autosteer technology for large tractors
    • 3.4.4. Tractor autosteer - a first step towards autonomy
    • 3.4.5. Semi-autonomous "follow-me" tractors
    • 3.4.6. EOX-175 by H2Trac
    • 3.4.7. Fully autonomous driverless tractors
    • 3.4.8. Autonomous tractor concepts developed by the major tractor companies
    • 3.4.9. When will fully autonomous tractors be ready?
    • 3.4.10. Monarch Tractor
    • 3.4.11. eTrac by Farmertronics
    • 3.4.12. AgBot by AgXeed
    • 3.4.13. Full automation of existing tractors
  • 3.5. Autonomous implement carriers and platform robots
    • 3.5.1. Small autonomous robots vs. tractors
    • 3.5.2. Land Care Robot by Directed Machines
    • 3.5.3. RoamIO by Korechi
    • 3.5.4. SwarmBot 5 by SwarmFarm Robotics
    • 3.5.5. Custom or standard implements?
    • 3.5.6. Dot by Raven Industries
    • 3.5.7. Robotti 150D by Agrointelli
    • 3.5.8. Over-the-row vineyard robots
    • 3.5.9. Bakus by VitiBot
    • 3.5.10. Ted by Naïo Technologies
    • 3.5.11. Trektor by SITIA
    • 3.5.12. A comparison of over-the-row vineyard robots
    • 3.5.13. Agricultural sprayer by Hubei Sense Intelligence Technology Co.
    • 3.5.14. A vision of the future? The Fendt MARS project
  • 3.6. Robotic fresh fruit and vegetable harvesting
    • 3.6.1. Row crop and non-fresh fruit harvesting is largely mechanised
    • 3.6.2. Fresh fruit picking remains largely manual
    • 3.6.3. Strawberries and apples: the most popular targets
    • 3.6.4. Robotic harvesting: apples
    • 3.6.5. FFRobot apple harvester by FFRobotics
    • 3.6.6. Robotic harvesting: strawberries
    • 3.6.7. A comparison of strawberry harvesting robot developers
    • 3.6.8. Strawberry picking robots in advanced development
    • 3.6.9. Harvester B7 by Harvest CROO Robotics
    • 3.6.10. Rubion by Octinion
    • 3.6.11. Robotic harvesting: asparagus
    • 3.6.12. Robotic asparagus harvesting projects
    • 3.6.13. Sparter by Cerescon
    • 3.6.14. Robotic harvesting in development for other crops
    • 3.6.15. Challenges in developing fruit picking robots
  • 3.7. Agricultural drones
    • 3.7.1. Drones: application pipeline
    • 3.7.2. Agricultural drones
    • 3.7.3. Commercially available agricultural drones
    • 3.7.4. Agricultural drones: key considerations
    • 3.7.5. Aerial imaging in farming
    • 3.7.6. Drones vs. satellites vs. aeroplanes
    • 3.7.7. Where does drone spraying have regulatory approval?
    • 3.7.8. Commercially available spraying drones
    • 3.7.9. Drones are becoming increasingly autonomous
    • 3.7.10. Agricultural drones: company landscape
    • 3.7.11. Potential software opportunities in agricultural drones
    • 3.7.12. Where do drones fit in on a farm?
    • 3.7.13. Fruit picking drones by Tevel Aerobotics Technologies
    • 3.7.14. CropHopper by HayBeeSee
  • 3.8. Milking robots and other robotic dairy farming
    • 3.8.1. Global trends and averages for dairy farm sizes
    • 3.8.2. Global number and distribution of dairy cows by territory
    • 3.8.3. Robotic (automatic) milking
    • 3.8.4. Robotic milking is becoming increasingly widespread
    • 3.8.5. Robotic milking: a blueprint for the wider agricultural robotics industry?
    • 3.8.6. Robotic milking: advantages and disadvantages
    • 3.8.7. Robotic milking: key players
    • 3.8.8. Robotic feed pushers
  • 3.9. Other applications
    • 3.9.1. PothaFacile by Pietro Rivi
    • 3.9.2. Tom by Small Robot Company
    • 3.9.3. Rock Picker by TerraClear

4. ENABLING TECHNOLOGIES

  • 4.1. Positioning technologies: RTK-GPS, LiDAR, and others
    • 4.1.1. Navigation for autonomous agricultural robots
    • 4.1.2. Navigation in agricultural environments
    • 4.1.3. The challenge of safe positioning
    • 4.1.4. Position accuracy vs. position integrity
    • 4.1.5. Achieving safe positioning
    • 4.1.6. Fixposition AG
    • 4.1.7. Agreenculture
    • 4.1.8. GPS as a tool for navigation
    • 4.1.9. RTK systems: operation, performance and value chain
    • 4.1.10. RTK systems for use in agriculture: value chain
    • 4.1.11. Challenges of RTK-GPS
    • 4.1.12. LiDAR
    • 4.1.13. LiDAR, Radar, camera & ultrasonic sensors: comparison
    • 4.1.14. Time of flight (TOF) LiDAR: Spatial Data Analysis
    • 4.1.15. Performance comparison of different LiDARs on the market or in development
    • 4.1.16. Assessing the suitability of different LiDAR for agricultural robotic applications
  • 4.2. Hyperspectral imaging
    • 4.2.1. Introduction to hyperspectral imaging
    • 4.2.2. Multiple methods to acquire a hyperspectral data-cube
    • 4.2.3. Line-scan hyperspectral camera design
    • 4.2.4. Snapshot hyperspectral imaging
    • 4.2.5. Illumination for hyperspectral imaging
    • 4.2.6. Hyperspectral imaging as a development of multispectral imaging
    • 4.2.7. Trade-offs between hyperspectral and multispectral imaging
    • 4.2.8. Hyperspectral imaging and precision agriculture
    • 4.2.9. Hyperspectral imaging from UAVs (drones)
    • 4.2.10. Satellite imaging with hyperspectral cameras
    • 4.2.11. Gamaya: Hyperspectral imaging for agricultural analysis
    • 4.2.12. Supplier overview: Hyperspectral imaging
  • 4.3. Artificial intelligence (AI)
    • 4.3.1. What is Artificial Intelligence?
    • 4.3.2. Key AI methods
    • 4.3.3. Main deep learning (DL) approaches
    • 4.3.4. DL makes automated image recognition possible
    • 4.3.5. Image recognition AI is based on convolutional neural networks (CNNs)
    • 4.3.6. Workings of CNNs: How are images processed?
    • 4.3.7. Workings of CNNs: An additional example
    • 4.3.8. Potential applications of machine learning in agriculture
    • 4.3.9. AI for weed recognition
    • 4.3.10. The challenge of image analysis
    • 4.3.11. Deepening the neural network to increase accuracy
    • 4.3.12. Deep learning: how accurate is "accurate enough"?
    • 4.3.13. AI in agricultural robotics case study - ecoRobotix: deep learning for crop and weed recognition
    • 4.3.14. AI in agricultural robotics case study - ecoRobotix: autonomous mobility
  • 4.4. End effectors and gripper technology
    • 4.4.1. End effector technology for fruit harvesting
    • 4.4.2. Designing a harvesting end effector
    • 4.4.3. End effectors for apple harvesting
    • 4.4.4. End effectors for tomato harvesting
    • 4.4.5. End effectors for cucumber harvesting
    • 4.4.6. End effectors for pepper (capsicum) harvesting
  • 4.5. Precision spraying technology
    • 4.5.1. What is precision spraying?
    • 4.5.2. Methods of spray control
    • 4.5.3. Pulse width modulation (PWM) spraying

5. MARKET FACTORS

  • 5.1. Market factors and business model considerations
    • 5.1.1. Which crop sectors will see agricultural robots first?
    • 5.1.2. Agricultural robotics and precision agriculture could lead to a new value chain emerging
    • 5.1.3. Development in agricultural robotics remains slow
    • 5.1.4. Revenues of major agricultural equipment suppliers
    • 5.1.5. Robotics-as-a-service (RaaS) vs. equipment sales
    • 5.1.6. Developing a successful business model
    • 5.1.7. Investment strategies in agricultural robotics
  • 5.2. Key market challenges in agricultural robotics
    • 5.2.1. The cost of agricultural robots
    • 5.2.2. IT infrastructure
    • 5.2.3. Ownership and management of digital data
    • 5.2.4. Adoption of robotics technology on farms

6. FORECASTS

  • 6.1. Agricultural robotics, market forecast by robot category
  • 6.2. Agricultural robotics, market forecast by robot category: data tables
  • 6.3. Agricultural robotics, market forecast by region
  • 6.4. Agricultural robotics, market forecast by region: data tables
  • 6.5. Milking robots, market forecast by region
  • 6.6. Weeding robots and seeding robots, market forecast by region
  • 6.7. Autonomous tractors and implement carrying robots, market forecast by region
  • 6.8. Robots for fresh fruit and vegetable harvesting, market forecast by region
  • 6.9. Agricultural drones, market forecast by region