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1859794

全球基於人工智慧的作物病害檢測市場：預測至2032年－按組件、病害類型、作物類型、技術、應用、最終用戶和地區進行分析

AI-Powered Crop Disease Detection Market Forecasts to 2032 - Global Analysis By Component, Disease Type, Crop Type, Technology, Application, 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

據 Stratistics MRC 稱，全球人工智慧作物病害檢測市場預計到 2025 年將達到 16 億美元，到 2032 年將達到 59 億美元，預測期內複合年成長率為 19.5%。

人工智慧驅動的作物病害檢測技術結合了電腦視覺、機器學習以及來自衛星、無人機和接近感測器的圖像，能夠識別病害、蟲害和營養壓力的早期症狀。自動化診斷有助於精準防治，減少作物損失，最佳化化學品使用，並實現更永續的干涉措施。隨著模型的改進、感測器的普及以及與農場管理平台的整合，市場應用將會不斷擴大。

根據《國際工程與技術研究期刊》(IRJET) 的報導，利用影像處理和機器學習的人工智慧作物病害檢測技術，在辨識小麥和水稻的葉枯病和銹病方面，準確率高達 92%。

加強糧食安全的必要性

全球人口成長和氣候壓力加劇了對可靠作物產量的需求，使得人工智慧驅動的病害檢測至關重要。農民、相關企業和政策制定者都在優先考慮能夠早期識別病原體的技術，以減少損失並提高糧食供應。此外，早期檢測還能減少化學投入，進而實現永續生產並降低成本。公共和私人對精密農業的投資將加速相關研究、部署和規模化推廣。這將促使更多商業農場和尋求建構彈性供應鏈的合作模式採用精準農業技術，同時提升全球農民的決策能力。

技術意識有限

人工智慧疾病檢測工具的普及應用受到許多農民技術素養低下和推廣支援不足的限制。小農戶可能缺乏智慧型手機、可靠的網路連接，或缺乏遵循自動化建議的信心，這限制了這些工具的實際效用。供應商在提供培訓、本地化介面和持續支援方面面臨更高的成本。此外，持懷疑態度的相關人員可能會抵制以數據主導的傳統耕作方式變革。要克服這些限制，需要進行有針對性的能力建設，與當地農業機構合作，採用以用戶為中心的設計，並部署價格合理、互聯互通的解決方案，以確保這些工具能夠得到切實有效的應用和持續推廣。

與農場管理軟體整合

將人工智慧病害檢測模組嵌入農場管理系統，可將診斷結果與日程安排、投入品採購和記錄保存關聯起來，從而提升價值。農民能夠收到基於上下文的建議，這些建議會將警報轉化為可執行的任務，例如有針對性地噴灑農藥或調整灌溉方式。這種整合簡化了工作流程，提高了買方的可追溯性，並支援認證系統。此外，此整合平台能夠提供豐富的資料集來最佳化模型，從而形成回饋循環，提高準確性。對於供應商而言，這種整合能夠帶來訂閱收入、交叉銷售，並有助於與世界各地的農業相關企業和合作社建立更深入的企業合作關係。

資料隱私和安全問題

收集田間影像、感測器資料流和管理記錄會產生高度敏感的資料集，如果處理不當，可能會損害人們對人工智慧作物監測服務的信任。農民擔心未授權存取、作物資訊的商業性用途以及衍生模型的所有權不明。不同司法管轄區的監管規定各不相同，這增加了國際供應商的合規負擔。此外，資料外洩和模型中毒等網路風險也可能擾亂營運。

新冠疫情的影響：

疫情凸顯了遠端自動化作物監測的價值，因為旅行限制和勞動力短缺限制了田間作業。儘管短期內部署有所延遲，但持續的投資轉向了人工智慧工具，這些工具減少了實地考察次數，並實現了連續監測。供應鏈的壓力增加了對早期檢測以保護產量的需求，而公共資金和研究夥伴關係則支持了試點計畫。整體而言，新冠疫情加速了數位農業的普及，並展現了數位農業在增強大小農場韌性方面的重要作用。

預計在預測期內，真菌病害細分市場將是最大的細分市場。

預計在預測期內，真菌病害領域將佔據最大的市場佔有率。銹病、霜霉病和晚疫病等病害對穀物、水果和蔬菜的產量造成了重大損失，因此對可靠診斷方法的需求持續旺盛。能夠檢測早期症狀的人工智慧解決方案可以減少活性化學品的使用，並提高作物品質。與應用平台和諮詢服務的整合進一步提升了投資報酬率。隨著資料集在不同地區的擴展，模型準確性不斷提高，這使得全球供應商提供的真菌檢測產品更受青睞。

預計在預測期內，軟體產業將實現最高的複合年成長率。

預計在預測期內，軟體產業將呈現最高的成長率。可擴展性、快速部署和持續學習循環使軟體對各種規模和地理的農場都極具吸引力。 SaaS 定價和雲端原生架構降低了前期投資，並有助於從試驗和試點階段過渡到規模化生產。與感測器和無人機的互通性提高了軟體的實用性，而利用新的田間資料定期重新訓練模型則提高了在當地條件下的偵測能力。由於農業技術投資者青睞輕資產平台，資本流動和夥伴關係有助於產品改進、市場拓展以及加速軟體主導解決方案的普及應用。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率。完善的農業技術生態系統、廣泛的互聯互通以及高度機械化，為人工智慧檢測平台的快速部署提供了有力支撐。大型商業農場和精密農業服務供應商正大力投資先進的感測、分析和決策支援工具，從而創造了巨大的市場需求。此外，強勁的私人投資、研究機構以及農業相關企業和商品買家有利的採購預算，也推動了供應商的創新。清晰的監管環境和完善的數據基礎設施，進一步促進了全部區域的規模部署和商業性夥伴關係。

複合年成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率。農業的快速數位化、智慧型手機普及率的不斷提高以及政府對精密農業的支持計劃，為人工智慧疾病檢測技術的普及創造了有利條件。龐大的小農戶群體為低成本、行動優先的解決方案提供了可擴展性，而本地新興企業正在根據當地作物和語言調整其模式。國際供應商正與經銷商和研究夥伴關係合作，以實現產品在地化。隨著基礎設施的改善和農業技術投資的增加，預計全部區域的普及率將加速提升。

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目錄

第1章執行摘要

第2章 引言

  • 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
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球人工智慧作物病害檢測市場（按組件分類）

  • 硬體
    • 相機
    • 無人機/無人飛行器
    • 智慧型手機和平板電腦
    • 加工設備和感測器
  • 軟體
    • 人工智慧/機器學習平台
    • 行動應用
    • 其他軟體
  • 服務
    • 整合和部署
    • 支援和維護
    • 諮詢和培訓

6. 全球人工智慧作物病害檢測市場（依病害類型分類）

  • 真菌病
  • 細菌性疾病
  • 病毒性疾病
  • 蟲害
  • 營養缺乏

7. 全球人工智慧作物病害檢測市場（依作物類型分類）

  • 糧食
  • 水果和蔬菜
  • 油籽/豆類
  • 經濟作物
  • 其他作物

8. 全球人工智慧作物病害檢測市場（依技術分類）

  • 機器學習/深度學習
    • 卷積類神經網路（CNN）
    • 循環神經網路（RNN）
    • 遷移學習
  • 電腦視覺
  • 預測分析
  • 自然語言處理

9. 全球人工智慧作物病害檢測市場（按應用領域分類）

  • 農田監測/勘察
  • 品質評價和產量監測
  • 農場層面建議、治療方案
  • 研究與開發

第10章 全球人工智慧作物病害檢測市場（依最終用戶分類）

  • 個體/小農戶
  • 大型企業農場和農業相關企業
  • 政府機構和研究機構
  • 農業合作社

第11章 全球人工智慧作物病害檢測市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 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12章：主要趨勢

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

第13章：企業概況

  • PEAT GmbH
  • Taranis
  • Prospera Technologies
  • Aerobotics
  • Sentera
  • AgroScout Ltd
  • Cropin Technology Solutions Pvt. Ltd.
  • IUNU Inc.
  • Fasal
  • Trace Genomics, Inc.
  • Gamaya SA
  • Picterra
  • HSAT
  • Agremo doo
  • Stenon GmbH
  • SkySquirrel Technologies Inc.
  • PlantVillage
Product Code: SMRC32018

According to Stratistics MRC, the Global AI-Powered Crop Disease Detection Market is accounted for $1.6 billion in 2025 and is expected to reach $5.9 billion by 2032, growing at a CAGR of 19.5% during the forecast period. AI-powered crop disease detection combines computer vision, machine learning, and imagery from satellites, drones, and proximal sensors to identify early symptoms of disease, pest infestation, and nutrient stress. Automated diagnostics support targeted treatments, lower crop losses, and optimize chemical usage, enabling more sustainable interventions. Market adoption grows with improved models, sensor accessibility, and integration into farm-management platforms.

According to the International Journal of Research in Engineering and Technology (IRJET), AI-based crop disease detection using image processing and machine learning has demonstrated up to 92% accuracy in identifying leaf blight and rust in wheat and rice.

Market Dynamics:

Driver:

Need for Enhanced Food Security

Rising global population and climate pressures are intensifying demand for reliable crop yields, making AI-powered disease detection essential. Farmers, agribusinesses, and policymakers prioritise technologies that identify pathogens early to reduce losses and improve food availability. Moreover, early detection lowers chemical input use, supporting sustainable production and cost savings. Public and private investment in precision agriculture accelerates research, deployment, and scale-up. Consequently, adoption increases across commercial farms and cooperative models seeking resilient supply chains while improving farmer decision-making capabilities globally.

Restraint:

Limited Technical Awareness

Adoption of AI disease-detection tools is constrained by low technical literacy among many growers and inadequate extension support. Smallholders may lack smartphones, reliable connectivity, or confidence to act on automated recommendations, limiting real-world effectiveness. Vendors face higher costs to provide training, localized interfaces, and ongoing support. Additionally, skeptical stakeholders may resist data-driven changes to traditional practices. Addressing this restraint requires targeted capacity building, partnerships with local agricultural agencies, and user-centred design to ensure practical, sustained uptake accompanied by affordable connectivity solutions.

Opportunity:

Integration with Farm Management Software

Embedding AI disease-detection modules within farm management systems amplifies value by linking diagnostics to scheduling, inputs procurement, and record-keeping. Farmers gain context-aware recommendations that translate alerts into actionable tasks, such as targeted spraying or altered irrigation. This integration streamlines workflows, improves traceability for buyers, and supports certification schemes. Additionally, combined platforms enable richer datasets for model refinement, creating feedback loops that enhance accuracy. For vendors, integrations open subscription revenue, cross-selling and deeper enterprise partnerships with agribusinesses and cooperatives globally.

Threat:

Data Privacy & Security Concerns

Harvesting field images, sensor streams, and management records creates sensitive datasets that, if mishandled, can undermine trust in AI crop-monitoring services. Farmers worry about unauthorized access, commercial exploitation of yield intelligence, and unclear ownership of derived models. Regulatory fragmentation across jurisdictions increases compliance burdens for vendors operating internationally. Moreover, cyber risks such as data leaks or model poisoning can disrupt operations.

Covid-19 Impact:

The pandemic highlighted the value of remote, automated crop monitoring as travel limits and labor shortages constrained field operations. Short-term deployment delays occurred, but sustained investment shifted toward AI tools that reduce visits and enable continuous surveillance. Supply-chain stress increased demand for early detection to protect yields, while public funding and research partnerships supported pilots. Overall, Covid-19 accelerated adoption and demonstrated digital agriculture's role in building resilience for small and large farms.

The fungal diseases segment is expected to be the largest during the forecast period

The fungal diseases segment is expected to account for the largest market share during the forecast period. Farmers confront significant yield losses from rusts, mildews, and blights across cereals, fruits, and vegetables, creating steady demand for reliable diagnostics. AI solutions that detect early symptomology reduce reactive chemical use and improve harvest quality, which buyers reward with premium pricing. Integration with spraying platforms and advisory services further enhances ROI. As datasets expand across geographies, model accuracy improves, reinforcing preference for fungal-focused detection offerings from suppliers globally.

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

Over the forecast period, the software segment is predicted to witness the highest growth rate. Scalability, rapid deployment, and continuous learning cycles make software attractive for diverse farm scales and geographies. SaaS pricing and cloud-native architectures reduce upfront capital, encouraging trials and pilot-to-scale transitions. Interoperability with sensors and drones increases utility, while regular model retraining with new field data improves detection under local conditions. As agritech investors favour asset-light platforms, capital flows and partnerships will fuel product enhancement, market reach, adoption velocity for software-led solutions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. Well-developed agricultural technology ecosystems, widespread connectivity, and high mechanization support rapid deployment of AI detection platforms. Large commercial farms and precision agriculture service providers invest in advanced sensing, analytics, and decision-support tools, generating significant market demand. Additionally, strong private investment, research institutions, and favourable procurement budgets among agribusinesses and commodity buyers drive vendor innovation. Regulatory clarity and data infrastructure further enable scalable rollouts and commercial partnerships across the region.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid digitalisation of agriculture, rising smartphone penetration, and government programs supporting precision farming create fertile conditions for AI disease detection uptake. Large populations of smallholder farmers present scalability opportunities for low-cost, mobile-first solutions, while local startups adapt models to regional crops and languages. Foreign vendors form partnerships with distributors and research institutes to localise offerings. As infrastructure improves and agtech investments increase, adoption rates are poised to accelerate across region.

Key players in the market

Some of the key players in AI-Powered Crop Disease Detection Market include PEAT GmbH, Taranis, Prospera Technologies, Aerobotics, Sentera, AgroScout Ltd, Cropin Technology Solutions Pvt. Ltd., IUNU Inc., Fasal, Trace Genomics, Inc., Gamaya SA, Picterra, HSAT, Agremo d.o.o., Stenon GmbH, SkySquirrel Technologies Inc., and PlantVillage.

Key Developments:

In August 2025, Launched Ag Assistant(TM), a generative AI agronomy engine that analyzes leaf-level imagery, weather, and machinery data to detect crop diseases and provide field-specific recommendations.

In May 2025, Picterra announced availability on Google Cloud Marketplace and its platform (GeoAI) supports automated detection/monitoring workflows used for plot monitoring and disease/pest detection; Picterra's news page lists the May 2025 item.

Components Covered:

  • Hardware
  • Software
  • Services

Disease Types Covered:

  • Fungal Diseases
  • Bacterial Diseases
  • Viral Diseases
  • Pest Infestation
  • Nutrient Deficiency

Crop Types Covered:

  • Cereals & Grains
  • Fruits & Vegetables
  • Oilseeds & Pulses
  • Cash Crops
  • Other Crops

Technologies Covered:

  • Machine Learning/Deep Learning
  • Computer Vision
  • Predictive Analytics
  • Natural Language Processing

Applications Covered:

  • Field Monitoring & Scouting
  • Quality Assessment & Yield Monitoring
  • Farm-level Advisory & Treatment Recommendations
  • Research & Development

End Users Covered:

  • Individual Farmers/Smallholders
  • Large-scale Corporate Farms & Agribusinesses
  • Government & Research Institutions
  • Agricultural Cooperatives

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 Technology Analysis
  • 3.7 Application 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 AI-Powered Crop Disease Detection Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
    • 5.2.1 Cameras
    • 5.2.2 Drones/UAVs
    • 5.2.3 Smartphones & Tablets
    • 5.2.4 Processing Units & Sensors
  • 5.3 Software
    • 5.3.1 AI/Machine Learning Platforms
    • 5.3.2 Mobile Applications
    • 5.3.3 Other Software
  • 5.4 Services
    • 5.4.1 Integration & Deployment
    • 5.4.2 Support & Maintenance
    • 5.4.3 Consulting & Training

6 Global AI-Powered Crop Disease Detection Market, By Disease Type

  • 6.1 Introduction
  • 6.2 Fungal Diseases
  • 6.3 Bacterial Diseases
  • 6.4 Viral Diseases
  • 6.5 Pest Infestation
  • 6.6 Nutrient Deficiency

7 Global AI-Powered Crop Disease Detection Market, By Crop Type

  • 7.1 Introduction
  • 7.2 Cereals & Grains
  • 7.3 Fruits & Vegetables
  • 7.4 Oilseeds & Pulses
  • 7.5 Cash Crops
  • 7.6 Other Crops

8 Global AI-Powered Crop Disease Detection Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning/Deep Learning
    • 8.2.1 Convolutional Neural Networks (CNNs)
    • 8.2.2 Recurrent Neural Networks (RNNs)
    • 8.2.3 Transfer Learning
  • 8.3 Computer Vision
  • 8.4 Predictive Analytics
  • 8.5 Natural Language Processing

9 Global AI-Powered Crop Disease Detection Market, By Application

  • 9.1 Introduction
  • 9.2 Field Monitoring & Scouting
  • 9.3 Quality Assessment & Yield Monitoring
  • 9.4 Farm-level Advisory & Treatment Recommendations
  • 9.5 Research & Development

10 Global AI-Powered Crop Disease Detection Market, By End User

  • 10.1 Introduction
  • 10.2 Individual Farmers/Smallholders
  • 10.3 Large-scale Corporate Farms & Agribusinesses
  • 10.4 Government & Research Institutions
  • 10.5 Agricultural Cooperatives

11 Global AI-Powered Crop Disease Detection Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 PEAT GmbH
  • 13.2 Taranis
  • 13.3 Prospera Technologies
  • 13.4 Aerobotics
  • 13.5 Sentera
  • 13.6 AgroScout Ltd
  • 13.7 Cropin Technology Solutions Pvt. Ltd.
  • 13.8 IUNU Inc.
  • 13.9 Fasal
  • 13.10 Trace Genomics, Inc.
  • 13.11 Gamaya SA
  • 13.12 Picterra
  • 13.13 HSAT
  • 13.14 Agremo d.o.o.
  • 13.15 Stenon GmbH
  • 13.16 SkySquirrel Technologies Inc.
  • 13.17 PlantVillage

List of Tables

  • Table 1 Global AI-Powered Crop Disease Detection Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Crop Disease Detection Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Crop Disease Detection Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI-Powered Crop Disease Detection Market Outlook, By Cameras (2024-2032) ($MN)
  • Table 5 Global AI-Powered Crop Disease Detection Market Outlook, By Drones/UAVs (2024-2032) ($MN)
  • Table 6 Global AI-Powered Crop Disease Detection Market Outlook, By Smartphones & Tablets (2024-2032) ($MN)
  • Table 7 Global AI-Powered Crop Disease Detection Market Outlook, By Processing Units & Sensors (2024-2032) ($MN)
  • Table 8 Global AI-Powered Crop Disease Detection Market Outlook, By Software (2024-2032) ($MN)
  • Table 9 Global AI-Powered Crop Disease Detection Market Outlook, By AI/Machine Learning Platforms (2024-2032) ($MN)
  • Table 10 Global AI-Powered Crop Disease Detection Market Outlook, By Mobile Applications (2024-2032) ($MN)
  • Table 11 Global AI-Powered Crop Disease Detection Market Outlook, By Other Software (2024-2032) ($MN)
  • Table 12 Global AI-Powered Crop Disease Detection Market Outlook, By Services (2024-2032) ($MN)
  • Table 13 Global AI-Powered Crop Disease Detection Market Outlook, By Integration & Deployment (2024-2032) ($MN)
  • Table 14 Global AI-Powered Crop Disease Detection Market Outlook, By Support & Maintenance (2024-2032) ($MN)
  • Table 15 Global AI-Powered Crop Disease Detection Market Outlook, By Consulting & Training (2024-2032) ($MN)
  • Table 16 Global AI-Powered Crop Disease Detection Market Outlook, By Disease Type (2024-2032) ($MN)
  • Table 17 Global AI-Powered Crop Disease Detection Market Outlook, By Fungal Diseases (2024-2032) ($MN)
  • Table 18 Global AI-Powered Crop Disease Detection Market Outlook, By Bacterial Diseases (2024-2032) ($MN)
  • Table 19 Global AI-Powered Crop Disease Detection Market Outlook, By Viral Diseases (2024-2032) ($MN)
  • Table 20 Global AI-Powered Crop Disease Detection Market Outlook, By Pest Infestation (2024-2032) ($MN)
  • Table 21 Global AI-Powered Crop Disease Detection Market Outlook, By Nutrient Deficiency (2024-2032) ($MN)
  • Table 22 Global AI-Powered Crop Disease Detection Market Outlook, By Crop Type (2024-2032) ($MN)
  • Table 23 Global AI-Powered Crop Disease Detection Market Outlook, By Cereals & Grains (2024-2032) ($MN)
  • Table 24 Global AI-Powered Crop Disease Detection Market Outlook, By Fruits & Vegetables (2024-2032) ($MN)
  • Table 25 Global AI-Powered Crop Disease Detection Market Outlook, By Oilseeds & Pulses (2024-2032) ($MN)
  • Table 26 Global AI-Powered Crop Disease Detection Market Outlook, By Cash Crops (2024-2032) ($MN)
  • Table 27 Global AI-Powered Crop Disease Detection Market Outlook, By Other Crops (2024-2032) ($MN)
  • Table 28 Global AI-Powered Crop Disease Detection Market Outlook, By Technology (2024-2032) ($MN)
  • Table 29 Global AI-Powered Crop Disease Detection Market Outlook, By Machine Learning/Deep Learning (2024-2032) ($MN)
  • Table 30 Global AI-Powered Crop Disease Detection Market Outlook, By Convolutional Neural Networks (CNNs) (2024-2032) ($MN)
  • Table 31 Global AI-Powered Crop Disease Detection Market Outlook, By Recurrent Neural Networks (RNNs) (2024-2032) ($MN)
  • Table 32 Global AI-Powered Crop Disease Detection Market Outlook, By Transfer Learning (2024-2032) ($MN)
  • Table 33 Global AI-Powered Crop Disease Detection Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 34 Global AI-Powered Crop Disease Detection Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 35 Global AI-Powered Crop Disease Detection Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 36 Global AI-Powered Crop Disease Detection Market Outlook, By Application (2024-2032) ($MN)
  • Table 37 Global AI-Powered Crop Disease Detection Market Outlook, By Field Monitoring & Scouting (2024-2032) ($MN)
  • Table 38 Global AI-Powered Crop Disease Detection Market Outlook, By Quality Assessment & Yield Monitoring (2024-2032) ($MN)
  • Table 39 Global AI-Powered Crop Disease Detection Market Outlook, By Farm-level Advisory & Treatment Recommendations (2024-2032) ($MN)
  • Table 40 Global AI-Powered Crop Disease Detection Market Outlook, By Research & Development (2024-2032) ($MN)
  • Table 41 Global AI-Powered Crop Disease Detection Market Outlook, By End User (2024-2032) ($MN)
  • Table 42 Global AI-Powered Crop Disease Detection Market Outlook, By Individual Farmers/Smallholders (2024-2032) ($MN)
  • Table 43 Global AI-Powered Crop Disease Detection Market Outlook, By Large-scale Corporate Farms & Agribusinesses (2024-2032) ($MN)
  • Table 44 Global AI-Powered Crop Disease Detection Market Outlook, By Government & Research Institutions (2024-2032) ($MN)
  • Table 45 Global AI-Powered Crop Disease Detection Market Outlook, By Agricultural Cooperatives (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.