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市場調查報告書
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2102408

基於人工智慧的作物病害檢測市場：預測至2034年——按組件、技術、應用、最終用戶和地區分類的全球分析

AI-Based Crop Disease Detection Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, AI Models, Imaging Devices, Cloud Platforms, Services and Mobile Applications), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

據 Stratistics MRC 稱，全球基於人工智慧的作物病害檢測市場預計將在 2026 年達到 3 億美元，並在預測期內以 29.2% 的複合年成長率成長，到 2034 年達到 14 億美元。

基於人工智慧的作物病害檢測是指應用人工智慧、機器學習演算法和電腦視覺技術，自動分析視覺、頻譜和感測器數據，以識別、分類和預測作物病害。這些系統處理無人機、智慧型手機和固定相機拍攝的影像，在人類觀察者能夠察覺病害之前，早期檢測出病害症狀。基於人工智慧的作物病害檢測平台利用在大量健康和患病植物影像資料集上訓練的深度學習模型，其診斷準確率可與植物病理學專家相媲美，甚至更高。

糧食安全請求

全球糧食需求的不斷成長，使得以人工智慧為基礎的作物病害檢測系統變得特別迫切，這類系統能夠有效防止產量損失，並保障農業產出。氣候變遷正在擴大作物病害的地理分佈範圍並加劇其嚴重性，從而產生新的威脅，而這些威脅無法透過傳統的監測方法有效應對。利用人工智慧進行病害早期檢測，能夠幫助農民在病害擴散前採取行動，從而維持作物質量，並減少對廣譜化學藥劑的需求。在主要農業經濟體中，數位化病害監測正日益被納入國家糧食安全戰略，成為建構韌性糧食系統的重要組成部分。

數據品質方面的限制

基於人工智慧的作物病害檢測系統的有效性從根本上受限於可用於演算法開發的訓練資料的品質和多樣性。對於許多作物和病害組合而言，缺乏足夠的標註影像來訓練穩健的檢測模型，這一趨勢在新興病原體和區域特異性病株中尤為突出。拍攝條件的變化，例如光照、天氣和相機質量，會導致結果不一致，並降低模型在實際農業環境中的表現。缺乏業界通用的標準化資料集和基準測試方案限制了不同人工智慧檢測解決方案之間的可比性和可靠性。

與物聯網生態系統整合

將基於人工智慧的作物病害檢測與更廣泛的農業物聯網生態系統相結合，為預測性病害管理和自動化干預創造了變革性的機會。監測環境狀況的互聯感測器網路可以啟動人工智慧病害模型，在症狀出現之前評估感染風險，從而實現預防性而非被動的管理策略。病害檢測與精準噴灑系統的融合，能夠實現標靶治療，在最大限度地提高療效的同時，最大限度地減少化學品的使用。聚合來自多個農場的病害檢測數據的雲端平台，正在產生寶貴的流行病學訊息，從而提高區域病害預測能力。

小規模的障礙

小規模農戶對以人工智慧為基礎的作物病害檢測技術的接受度有限，這對市場成長和農業技術的公平分配構成了重大威脅。小規模農戶往往缺乏有效利用人工智慧偵測應用所需的數位素養、智慧型手機和網路連線。許多商業人工智慧病害檢測平台採用的訂閱式定價模式，對於利潤微薄的小農戶而言難以負擔。除非採取有針對性的措施來解決經濟負擔和取得方面的難題，否則人工智慧病害檢測技術很可能仍將集中在大規模商業農場。

新型冠狀病毒（COVID-19）的影響：

新冠疫情擾亂了農業推廣服務和實地考察，導致人們對基於人工智慧的遠端作物病害監測解決方案的興趣日益濃厚。封鎖措施使得農業專家無法到農場進行實地考察，迫使農民採用無需面對面諮詢即可識別病害的數位診斷工具。疫情後，遠距農業諮詢服務的建立進一步推動了對基於人工智慧的病害檢測平台的持續需求。在一些國家，政府的復甦計畫資助了包括人工智慧病害監測能力在內的數位化農業舉措。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率，這主要得益於人工智慧疾病檢測平台持續的收入模式，以及與專有機器學習模型和演算法相關的高額智慧財產權價值。軟體解決方案透過訂閱和授權協議實現高價定價，為技術供應商提供了永續的收入來源。基於機器學習的檢測演算法的不斷改進正在創造永續的價值，從而證明了持續軟體投資的合理性。大型農業業者正在優先考慮將疾病檢測功能與更廣泛的農場管理系統整合的綜合軟體平台。

預計在預測期內，邊緣人工智慧領域將呈現最高的複合年成長率。

在預測期內，邊緣人工智慧領域預計將呈現最高的成長率，這主要得益於偏遠農業地區（網路連線有限）對即時病害檢測的需求，以及邊緣運算硬體成本的不斷下降。邊緣人工智慧無需依賴雲端連接即可在設備上進行作物影像處理，從而解決了農村農業環境中普遍存在的延遲和連接難題。神經網路架構的小型化使得即使在智慧型手機和嵌入式感測器等低功耗邊緣設備上也能實現先進的病害檢測。農業技術供應商正日益重視邊緣部署策略，以應對網路基礎設施不可靠的市場。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率。這主要歸功於該地區聚集了許多領先的人工智慧技術公司，例如IBM公司、微軟公司和谷歌公司，這些公司致力於開發先進的作物病害檢測解決方案。在美國，大規模的商業農業發展迅速，推動了技術普及率的提高，並使其擁有足夠的財力投資於基於人工智慧的農業工具。在加拿大，先進的農業研究生態系統和政府對精密農業創新的支持，為基於人工智慧的病害檢測技術的應用創造了有利條件。在墨西哥，蓬勃發展的商業農業部門正擴大採用數位化工具進行作物保護和病害管理。

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。這主要歸因於中國、印度和東南亞國家龐大的農業基礎，以及這些地區每年因作物病害造成的重大產量損失。中國的國家數位農業策略包括對基於人工智慧的作物監測和病害檢測系統進行大量投資。在印度，一個蓬勃發展的農業科技新創企業生態系統正在開發價格合理的、針對當地作物品種和農業條件量身定做的人工智慧病害檢測解決方案。東南亞各國政府正在實施以技術主導的農業轉型計劃，優先發展數位化作物保護和病害預警系統。

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目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章 全球以人工智慧為基礎的作物病害檢測市場：按組件分類

  • 軟體
  • 硬體
  • 人工智慧模型
  • 影像設備
  • 雲端平台
  • 服務
  • 行動應用

第6章：全球以人工智慧為基礎的作物病害檢測市場：依技術分類

  • 機器學習
  • 深度學習
  • 電腦視覺
  • 高光譜影像
  • 無人機分析
  • 邊緣人工智慧

第7章 全球基於人工智慧的作物病害檢測市場：按應用領域分類

  • 田間作物監測
  • 溫室監測
  • 果樹監測
  • 蔬菜作物監測
  • 種子品質評價
  • 疾病預測
  • 病蟲害鑑定

第8章：全球基於人工智慧的作物病害檢測市場：按最終用戶分類

  • 商業農場
  • 溫室經營者
  • 農業合作社
  • 研究機構
  • 政府機構
  • 農業技術公司

第9章：全球基於人工智慧的作物病害檢測市場：按地區分類

  • 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10章 戰略市場資訊

  • 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11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12章：公司簡介

  • Bayer AG
  • Corteva, Inc.
  • Syngenta AG
  • BASF SE
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Plantix（PEAT GmbH）
  • Prospera Technologies Ltd.
  • Taranis Visual Ltd.
  • Cropin Technology Solutions Pvt. Ltd.
  • PrecisionHawk
  • John Deere
  • Trimble Inc.
  • SenseFly SA
  • Xarvio Digital Farming Solutions
  • Agremo
Product Code: SMRC38291

According to Stratistics MRC, the Global AI-Based Crop Disease Detection Market is accounted for $0.3 billion in 2026 and is expected to reach $1.4 billion by 2034 growing at a CAGR of 29.2% during the forecast period. AI-based crop disease detection refers to the application of artificial intelligence, machine learning algorithms, and computer vision technologies to identify, classify, and predict plant diseases in agricultural crops through automated analysis of visual, spectral, and sensor data. These systems process images captured by drones, smartphones, and fixed cameras to detect disease symptoms at early stages before visible damage becomes apparent to human observers. AI-based crop disease detection platforms leverage deep learning models trained on extensive datasets of healthy and diseased plant imagery to achieve diagnostic accuracy that rivals or exceeds expert plant pathologists.

Market Dynamics:

Driver:

Food Security Imperatives

The escalating global demand for food production is driving urgent adoption of AI-based crop disease detection systems that can prevent yield losses and protect agricultural output. Climate change is expanding the geographic range and severity of crop diseases, creating new threats that traditional monitoring methods cannot address effectively. Early disease detection enabled by artificial intelligence allows farmers to intervene before infections spread, preserving crop quality and reducing the need for broad-spectrum chemical treatments. National food security strategies in major agricultural economies are increasingly incorporating digital disease surveillance as a critical component of resilient food systems.

Restraint:

Data Quality Limitations

The effectiveness of AI-based crop disease detection systems is fundamentally constrained by the quality and diversity of training data available for algorithm development. Many crop-disease combinations lack sufficient annotated imagery to train robust detection models, particularly for emerging pathogens and region-specific disease strains. Variable imaging conditions, including lighting, weather, and camera quality, introduce inconsistencies that degrade model performance in real-world agricultural settings. The absence of standardized datasets and benchmarking protocols across the industry limits the comparability and reliability of different AI detection solutions.

Opportunity:

Integration with IoT Ecosystems

The integration of AI-based crop disease detection with broader agricultural Internet of Things ecosystems is creating transformative opportunities for predictive disease management and automated intervention. Connected sensor networks that monitor environmental conditions can trigger AI disease models to assess infection risk before symptoms appear, enabling proactive rather than reactive management strategies. The convergence of disease detection with precision spraying systems allows for targeted treatment application that minimizes chemical usage while maximizing efficacy. Cloud-based platforms that aggregate disease detection data across multiple farms are generating valuable epidemiological insights that improve regional disease forecasting capabilities.

Threat:

Adoption Barriers for Smallholders

The limited adoption of AI-based crop disease detection among smallholder farmers represents a significant threat to market growth and equitable agricultural technology distribution. Small-scale farmers often lack the digital literacy, smartphone access, and internet connectivity required to utilize AI detection applications effectively. The subscription-based pricing models of many commercial AI disease detection platforms are financially inaccessible for farmers operating on thin profit margins. Without targeted interventions to address affordability and accessibility challenges, AI disease detection may remain concentrated among large commercial operations.

Covid-19 Impact:

The COVID-19 pandemic disrupted agricultural extension services and field scouting activities, creating conditions that accelerated interest in remote AI-based crop disease monitoring solutions. Lockdown measures prevented agricultural experts from visiting farms, prompting farmers to adopt digital diagnostic tools that could provide disease identification without in-person consultation. Post-pandemic, the normalization of remote agricultural advisory services has sustained demand for AI-powered disease detection platforms. Government recovery programs in several countries have directed funding toward digital agriculture initiatives that include AI disease surveillance capabilities.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, due to the recurring revenue model of AI disease detection platforms and the high value of proprietary machine learning models and algorithmic intellectual property. Software solutions command premium pricing through subscription and licensing arrangements that generate sustained revenue streams for technology providers. The continuous improvement of detection algorithms through machine learning creates ongoing value that justifies recurring software investments. Enterprise agricultural operations prioritize comprehensive software platforms that integrate disease detection with broader farm management systems.

The edge AI segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the edge AI segment is predicted to witness the highest growth rate, driven by the need for real-time disease detection in remote agricultural areas with limited internet connectivity and the declining cost of edge computing hardware. Edge AI enables on-device processing of crop imagery without dependency on cloud connectivity, addressing critical latency and connectivity challenges in rural farming environments. The miniaturization of neural network architectures is making sophisticated disease detection feasible on low-power edge devices, including smartphones and embedded sensors. Agricultural technology providers are increasingly prioritizing edge deployment strategies to serve markets with unreliable internet infrastructure.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the concentration of leading AI technology companies including IBM Corporation, Microsoft Corporation, and Google LLC that are developing advanced crop disease detection solutions. The United States maintains extensive commercial farming operations with strong technology adoption rates and the financial capacity to invest in AI-powered agricultural tools. Canada's advanced agricultural research ecosystem and government support for precision agriculture innovation create favorable conditions for AI disease detection deployment. Mexico's growing commercial agriculture sector is increasingly adopting digital tools for crop protection and disease management.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to the vast agricultural base across China, India, and Southeast Asian nations, where crop disease causes significant annual yield losses. China's national digital agriculture strategy includes substantial investments in AI-powered crop monitoring and disease surveillance systems. India's expanding agricultural technology startup ecosystem is developing affordable AI disease detection solutions tailored to local crop varieties and farming conditions. Southeast Asian governments are implementing technology-driven agricultural transformation programs that prioritize digital crop protection and disease early warning systems.

Key players in the market

Some of the key players in AI-Based Crop Disease Detection Market include Bayer AG, Corteva, Inc., Syngenta AG, BASF SE, IBM Corporation, Microsoft Corporation, Google LLC, Plantix (PEAT GmbH), Prospera Technologies Ltd., Taranis Visual Ltd., Cropin Technology Solutions Pvt. Ltd., PrecisionHawk, John Deere, Trimble Inc., SenseFly SA, Xarvio Digital Farming Solutions and Agremo.

Key Developments:

In June 2026, Bayer AG launched an AI-powered disease prediction platform integrating satellite imagery with ground-level sensor data to forecast fungal disease outbreaks across major cereal crops with seventy-two hour warning capabilities.

In May 2026, IBM Corporation expanded its Watson agriculture platform with advanced computer vision models capable of identifying forty-seven crop diseases across twelve major food crops through smartphone camera analysis.

In April 2026, Plantix (PEAT GmbH) introduced a real-time disease detection API for agricultural drones enabling automated aerial crop scouting with sub-millimeter symptom identification across large commercial farming operations.

Components Covered:

  • Software
  • Hardware
  • AI Models
  • Imaging Devices
  • Cloud Platforms
  • Services
  • Mobile Applications

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Hyperspectral Imaging
  • Drone Analytics
  • Edge AI

Applications Covered:

  • Field Crop Monitoring
  • Greenhouse Monitoring
  • Fruit Crop Monitoring
  • Vegetable Crop Monitoring
  • Seed Quality Assessment
  • Disease Forecasting
  • Pest & Disease Identification

End Users Covered:

  • Commercial Farms
  • Greenhouse Operators
  • Agricultural Cooperatives
  • Research Institutes
  • Government Agencies
  • AgriTech Companies

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

Table of Contents

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 AI-Based Crop Disease Detection Market, By Component

  • 5.1 Software
  • 5.2 Hardware
  • 5.3 AI Models
  • 5.4 Imaging Devices
  • 5.5 Cloud Platforms
  • 5.6 Services
  • 5.7 Mobile Applications

6 Global AI-Based Crop Disease Detection Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Computer Vision
  • 6.4 Hyperspectral Imaging
  • 6.5 Drone Analytics
  • 6.6 Edge AI

7 Global AI-Based Crop Disease Detection Market, By Application

  • 7.1 Field Crop Monitoring
  • 7.2 Greenhouse Monitoring
  • 7.3 Fruit Crop Monitoring
  • 7.4 Vegetable Crop Monitoring
  • 7.5 Seed Quality Assessment
  • 7.6 Disease Forecasting
  • 7.7 Pest & Disease Identification

8 Global AI-Based Crop Disease Detection Market, By End User

  • 8.1 Commercial Farms
  • 8.2 Greenhouse Operators
  • 8.3 Agricultural Cooperatives
  • 8.4 Research Institutes
  • 8.5 Government Agencies
  • 8.6 AgriTech Companies

9 Global AI-Based Crop Disease Detection Market, By Geography

  • 9.1 North America
    • 9.1.1 United States
    • 9.1.2 Canada
    • 9.1.3 Mexico
  • 9.2 Europe
    • 9.2.1 United Kingdom
    • 9.2.2 Germany
    • 9.2.3 France
    • 9.2.4 Italy
    • 9.2.5 Spain
    • 9.2.6 Netherlands
    • 9.2.7 Belgium
    • 9.2.8 Sweden
    • 9.2.9 Switzerland
    • 9.2.10 Poland
    • 9.2.11 Rest of Europe
  • 9.3 Asia Pacific
    • 9.3.1 China
    • 9.3.2 Japan
    • 9.3.3 India
    • 9.3.4 South Korea
    • 9.3.5 Australia
    • 9.3.6 Indonesia
    • 9.3.7 Thailand
    • 9.3.8 Malaysia
    • 9.3.9 Singapore
    • 9.3.10 Vietnam
    • 9.3.11 Rest of Asia Pacific
  • 9.4 South America
    • 9.4.1 Brazil
    • 9.4.2 Argentina
    • 9.4.3 Colombia
    • 9.4.4 Chile
    • 9.4.5 Peru
    • 9.4.6 Rest of South America
  • 9.5 Rest of the World (RoW)
    • 9.5.1 Middle East
      • 9.5.1.1 Saudi Arabia
      • 9.5.1.2 United Arab Emirates
      • 9.5.1.3 Qatar
      • 9.5.1.4 Israel
      • 9.5.1.5 Rest of Middle East
    • 9.5.2 Africa
      • 9.5.2.1 South Africa
      • 9.5.2.2 Egypt
      • 9.5.2.3 Morocco
      • 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

  • 10.1 Industry Value Network and Supply Chain Assessment
  • 10.2 White-Space and Opportunity Mapping
  • 10.3 Product Evolution and Market Life Cycle Analysis
  • 10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

  • 11.1 Mergers and Acquisitions
  • 11.2 Partnerships, Alliances, and Joint Ventures
  • 11.3 New Product Launches and Certifications
  • 11.4 Capacity Expansion and Investments
  • 11.5 Other Strategic Initiatives

12 Company Profiles

  • 12.1 Bayer AG
  • 12.2 Corteva, Inc.
  • 12.3 Syngenta AG
  • 12.4 BASF SE
  • 12.5 IBM Corporation
  • 12.6 Microsoft Corporation
  • 12.7 Google LLC
  • 12.8 Plantix (PEAT GmbH)
  • 12.9 Prospera Technologies Ltd.
  • 12.10 Taranis Visual Ltd.
  • 12.11 Cropin Technology Solutions Pvt. Ltd.
  • 12.12 PrecisionHawk
  • 12.13 John Deere
  • 12.14 Trimble Inc.
  • 12.15 SenseFly SA
  • 12.16 Xarvio Digital Farming Solutions
  • 12.17 Agremo

List of Tables

Tabl 1 Global AI-Based Crop Disease Detection Market Outlook, By Region (2023-2034) ($MN)

  • Table 2 Global AI-Based Crop Disease Detection Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Based Crop Disease Detection Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI-Based Crop Disease Detection Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 5 Global AI-Based Crop Disease Detection Market Outlook, By AI Models (2023-2034) ($MN)
  • Table 6 Global AI-Based Crop Disease Detection Market Outlook, By Imaging Devices (2023-2034) ($MN)
  • Table 7 Global AI-Based Crop Disease Detection Market Outlook, By Cloud Platforms (2023-2034) ($MN)
  • Table 8 Global AI-Based Crop Disease Detection Market Outlook, By Services (2023-2034) ($MN)
  • Table 9 Global AI-Based Crop Disease Detection Market Outlook, By Mobile Applications (2023-2034) ($MN)
  • Table 10 Global AI-Based Crop Disease Detection Market Outlook, By Technology (2023-2034) ($MN)
  • Table 11 Global AI-Based Crop Disease Detection Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 12 Global AI-Based Crop Disease Detection Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 13 Global AI-Based Crop Disease Detection Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 14 Global AI-Based Crop Disease Detection Market Outlook, By Hyperspectral Imaging (2023-2034) ($MN)
  • Table 15 Global AI-Based Crop Disease Detection Market Outlook, By Drone Analytics (2023-2034) ($MN)
  • Table 16 Global AI-Based Crop Disease Detection Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 17 Global AI-Based Crop Disease Detection Market Outlook, By Application (2023-2034) ($MN)
  • Table 18 Global AI-Based Crop Disease Detection Market Outlook, By Field Crop Monitoring (2023-2034) ($MN)
  • Table 19 Global AI-Based Crop Disease Detection Market Outlook, By Greenhouse Monitoring (2023-2034) ($MN)
  • Table 20 Global AI-Based Crop Disease Detection Market Outlook, By Fruit Crop Monitoring (2023-2034) ($MN)
  • Table 21 Global AI-Based Crop Disease Detection Market Outlook, By Vegetable Crop Monitoring (2023-2034) ($MN)
  • Table 22 Global AI-Based Crop Disease Detection Market Outlook, By Seed Quality Assessment (2023-2034) ($MN)
  • Table 23 Global AI-Based Crop Disease Detection Market Outlook, By Disease Forecasting (2023-2034) ($MN)
  • Table 24 Global AI-Based Crop Disease Detection Market Outlook, By Pest & Disease Identification (2023-2034) ($MN)
  • Table 25 Global AI-Based Crop Disease Detection Market Outlook, By End User (2023-2034) ($MN)
  • Table 26 Global AI-Based Crop Disease Detection Market Outlook, By Commercial Farms (2023-2034) ($MN)
  • Table 27 Global AI-Based Crop Disease Detection Market Outlook, By Greenhouse Operators (2023-2034) ($MN)
  • Table 28 Global AI-Based Crop Disease Detection Market Outlook, By Agricultural Cooperatives (2023-2034) ($MN)
  • Table 29 Global AI-Based Crop Disease Detection Market Outlook, By Research Institutes (2023-2034) ($MN)
  • Table 30 Global AI-Based Crop Disease Detection Market Outlook, By Government Agencies (2023-2034) ($MN)
  • Table 31 Global AI-Based Crop Disease Detection Market Outlook, By AgriTech Companies (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.