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2144486

作物健康預測市場預測至2034年—全球疾病、預測方法、資料輸入、預測輸出、最終用戶和區域分析

Predictive Crop Health Market Forecasts to 2034 - Global Analysis By Disease (Fungal Diseases, Bacterial Diseases, Viral Diseases, Nematode Diseases and Other Diseases), Forecasting Method, Data Input, Forecast Output, End User, and Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球作物健康預測管理市場預計將在 2026 年達到 6 億美元，並在預測期內以 16.3% 的複合年成長率成長，到 2034 年達到 20 億美元。

預測性作物健康管理是指利用農業和環境數據，透過數位化工具和分析系統來預測作物病害發生的機率、時間和潛在嚴重性。這些解決方案整合了天氣狀況、濕度、溫度、土壤資訊、作物生長階段、歷史病害模式、遙感探測和人工智慧等訊息，從而產生早期預警和風險評估。預測性作物健康管理使農民能夠及時採取作物保護措施，減少不必要的農藥使用，最大限度地減少產量損失，並改善農場管理決策。精密農業的日益普及和氣候變遷正在推動對預測性作物病害管理技術的需求。

市場動態

氣候變遷加劇和疾病壓力上升

氣候變遷加劇和天氣模式的轉變為作物病害爆發創造了有利條件，推動了對能夠實現病害預防管理的預測性解決方案的需求。農民正在尋求早期預警系統，以最佳化殺菌劑的使用並減少作物損失。氣候變遷正在擴大病害爆發的範圍，為病害控制帶來新的挑戰。作物價值和生產成本的上漲增加了病害預防的經濟獎勵。確保產量對農場盈利而言變得越來越重要。

模型精度和數據可用性的局限性

疾病預測模型的準確性不足以及數據可用性的限制制約了預測方案在不同種植系統中的實施和有效性。預測模型需要大量的區域資料進行校準和檢驗，但並非所有地區都能獲得這些資料。天氣、作物和病原體等因素之間的複雜相互作用也為建模帶來了挑戰。發展中地區的數據匱乏限制了預測的可用性和準確性。模型檢驗和不確定性的量化仍然是技術難題。

人工智慧與遙感探測技術的融合

將人工智慧 (AI) 與遙感探測技術相結合，以增強疾病檢測和預測能力，為解決方案供應商帶來了巨大的成長機會。人工智慧模型可以分析衛星和感測器數據，及早發現疾病，甚至在症狀出現之前就能進行檢測。基於智慧型手機的疾病識別和預測應用程式的開發，正在擴大小規模農戶的使用範圍。與農場管理平台的整合，正在建立全面的決策支援解決方案。隨著人工智慧能力的不斷發展，疾病預測的準確性也持續提高。

與替代疾病管理方法的競爭

來自其他病害管理方法（例如抗病品種和化學防治）的競爭可能會限制預測解決方案的採用。農民對預測益處的認知不足也可能阻礙其應用。經濟壓力可能導致農民優先考慮低成本的病害管理方法。農村地區通訊基礎設施有限可能會影響即時預測資訊的提供。技術推廣障礙可能會限制市場滲透率。

新型冠狀病毒（COVID-19）的影響：

新冠感染疾病凸顯了糧食系統韌性的重要性以及有效作物病害管理的必要性。在旅行限制期間，用於遠端決策支援的數位化預測解決方案的重要性日益凸顯。疫情後時期，人們對精密農業和數位化諮詢服務的興趣持續高漲。對糧食安全和氣候變遷調適的日益重視正在推動市場成長。數位化農業在整個農業領域的應用正在加速推進。

在預測期內，真菌病害領域預計將佔據最大的市場佔有率。

預計在預測期內，真菌病害領域將佔據最大的市場佔有率。這是因為真菌病害是全球作物生產面臨的最嚴重病害威脅，而且針對主要作物和病原體系統，已建立了成熟的預測模型和歷史病害數據。真菌病害會導致作物產量大幅下降，需要根據病害風險評估及時施用殺菌劑。廣泛的研究和建模經驗是真菌病害預測領域領先地位的基石。真菌病害預測擁有最成熟的科學基礎。真菌病害造成的產量損失大大提高了農民對病害預測的關注。

在預測期內，機器學習領域預計將呈現最高的複合年成長率。

在預測期內，機器學習領域預計將呈現最高的成長率，這主要得益於大規模資料集的日益豐富和運算能力的提升，從而能夠建立更精準的疾病預測模型。機器學習演算法能夠辨識環境和疾病資料中的複雜模式，提高預測精度。數位農業平台的日益普及正在產生訓練機器學習模型所需的數據。在疾病預測方面，機器學習的表現已超越傳統模型。隨著數據可用性的不斷提高，機器學習的應用範圍正在擴展到所有種植系統。

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，這主要得益於先進農業技術的應用、完善的作物病害研究基礎設施以及農民對精密農業解決方案的高度認知。美國擁有眾多領先的農業技術公司和病害預測服務供應商，並建立了成熟的農民網路和數據平台。強大的農業研究和推廣基礎設施鞏固了其在該地區市場的領先地位。大規模商業農業正在推動全部區域病害預測技術的應用。

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，這主要得益於農業技術的快速普及、對糧食安全日益成長的擔憂以及氣候變遷對主要經濟體作物病害的影響。中國、印度和東南亞國家正在提升病害預測能力，以支持小規模。作物集約化程度的提高和氣候變遷導致的日益加劇的波動性正在加速全部區域的市場成長。政府對農業數位化的支持正在推動亞洲市場對數位化技術的廣泛應用。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
    • 根據產品系列、企業發展和策略聯盟對重點公司進行基準分析。

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：全球作物健康預測市場：依疾病分類

  • 真菌病
  • 細菌性疾病
  • 病毒性疾病
  • 線蟲病
  • 其他疾病

第6章：全球作物健康預測市場：依預測方法分類

  • 天氣模型
  • 統計模型
  • 機器學習
  • 深度學習
  • 混合模式
  • 其他預測方法

第7章：全球作物健康預測市場：依資料輸入

  • 天氣數據
  • 衛星數據
  • 作物數據
  • 土壤數據
  • 現場感測器數據
  • 其他資料錄入

第8章：全球作物健康預測市場：依預測輸出分類

  • 風險評分
  • 疾病預警
  • 事件狀態圖
  • 嚴重性預測
  • 處理時間
  • 其他預測指標

第9章 全球作物健康預測市場：依最終用戶分類

  • 農民
  • 農業科學家
  • 作物顧問
  • 農業化學品製造商
  • 政府機構
  • 其他最終用戶

第10章：全球作物健康預測市場：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11章 策略市場資訊

  • 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12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13章：公司簡介

  • Climate FieldView
  • Plantix
  • AgroStar
  • Prospera Technologies Ltd.
  • Arable Labs, Inc.
  • Taranis Visual Ltd.
  • PEAT GmbH
  • PlantVillage
  • Trace Genomics, Inc.
  • OneSoil
  • IBM Corporation
  • John Deere
  • Trimble Inc.
  • AgriTask
  • CropX Technologies Ltd.
Product Code: SMRC39845

According to Stratistics MRC, the Global Predictive Crop Health Market is accounted for $0.6 billion in 2026 and is expected to reach $2.0 billion by 2034 growing at a CAGR of 16.3% during the forecast period. Predictive Crop Health comprises digital tools and analytical systems that predict the likelihood, timing, and potential severity of crop diseases using agricultural and environmental data. These solutions integrate weather conditions, humidity, temperature, soil information, crop growth stages, historical disease patterns, remote sensing, and artificial intelligence to generate early warnings and risk assessments. Predictive Crop Health enables farmers to implement timely crop protection measures, reduce unnecessary chemical applications, limit yield losses, and improve farm management decisions. Increasing adoption of precision agriculture and climate variability are driving demand for predictive crop disease management technologies.

Market Dynamics

Driver:

Growing climate variability and disease pressure

Increasing climate variability and changing weather patterns are creating more favorable conditions for crop diseases, driving demand for forecasting solutions that enable proactive disease management. Farmers are seeking early warning systems to optimize fungicide applications and reduce crop losses. Climate change is expanding disease ranges and creating new disease challenges. Growing crop value and input costs are increasing economic incentives for disease prevention. Yield protection is becoming increasingly important for farm profitability.

Restraint:

Limited model accuracy and data availability

Limited model accuracy and data availability for disease forecasting constrain adoption and effectiveness of forecasting solutions across diverse cropping systems. Forecasting models require extensive localized data for calibration and validation that may not be available in all regions. Complex interactions between weather, crop, and pathogen factors create modeling challenges. Data gaps in developing regions limit forecast availability and accuracy. Model validation and uncertainty quantification remain technical challenges.

Opportunity:

Integration of AI and remote sensing technologies

Integration of artificial intelligence and remote sensing technologies for enhanced disease detection and forecasting presents significant growth opportunities for solution providers. AI models can analyze satellite and sensor data for early disease detection before visible symptoms appear. Development of smartphone-based disease identification and forecasting applications is expanding access to smallholder farmers. Integration with farm management platforms is creating comprehensive decision support solutions. AI capabilities continue improving disease forecasting accuracy.

Threat:

Competition from alternative disease management approaches

Competition from alternative disease management approaches including resistant varieties and chemical treatments may limit forecasting solution adoption. Limited farmer awareness of forecasting benefits may constrain adoption. Economic pressures may lead farmers to prioritize lower-cost disease management approaches. Connectivity limitations in rural areas may affect real-time forecast delivery. Technology adoption barriers may limit market penetration.

Covid-19 Impact:

The COVID-19 pandemic highlighted the importance of food system resilience and the need for effective crop disease management. Digital forecasting solutions gained importance for remote decision support during movement restrictions. The post-pandemic period has witnessed sustained interest in precision agriculture and digital advisory services. Growing focus on food security and climate adaptation supports market growth. Digital agriculture adoption has accelerated across farming sectors.

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 as fungal diseases represent the most significant disease threat to crop production globally with established forecasting models and historical disease data for major crop-pathogen systems. Fungal diseases cause substantial yield losses and require timely fungicide applications based on disease risk assessment. Extensive research and modeling experience support fungal disease forecasting leadership. Fungal disease forecasting has the most established scientific foundation. Yield losses from fungal diseases drive significant farmer interest in forecasting.

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

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate driven by increasing availability of large datasets and computing power enabling more accurate disease forecasting models. Machine learning algorithms can identify complex patterns in environmental and disease data for improved prediction accuracy. Growing adoption of digital agriculture platforms is generating data for machine learning model training. Machine learning is outperforming traditional models for disease prediction. Data availability is expanding machine learning applications across cropping systems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to advanced agricultural technology adoption, extensive crop disease research infrastructure, and high farmer awareness of precision agriculture solutions. The United States hosts major agricultural technology companies and disease forecasting service providers with established farmer networks and data platforms. Strong agricultural research and extension infrastructure reinforce regional market leadership. Large-scale commercial farming drives disease forecasting adoption 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 agricultural technology adoption, growing food security concerns, and climate change impacts on crop diseases across major economies. China, India, and Southeast Asian countries are expanding disease forecasting capabilities to support smallholder farmers. Growing crop intensification and climate variability are accelerating market growth across the region. Government support for agricultural digitalization is increasing adoption across Asian markets.

Key players in the market

Some of the key players in the Predictive Crop Health Market include Climate FieldView, Plantix, AgroStar, Prospera Technologies Ltd., Arable Labs, Inc., Taranis Visual Ltd., PEAT GmbH, PlantVillage, Trace Genomics, Inc., OneSoil, IBM Corporation, John Deere, Trimble Inc., AgriTask, and CropX Technologies Ltd.

Key Developments:

In May 2025, Climate FieldView launched an enhanced disease forecasting platform integrating AI-powered risk prediction and real-time disease alerts for major cropping systems. The platform provides actionable recommendations for fungicide timing and disease management.

In April 2025, Taranis Visual Ltd. announced significant enhancements to its disease forecasting capabilities with new remote sensing and AI detection features enabling earlier disease identification and intervention.

Diseases Covered:

  • Fungal Diseases
  • Bacterial Diseases
  • Viral Diseases
  • Nematode Diseases
  • Other Diseases

Forecasting Methods Covered:

  • Weather Models
  • Statistical Models
  • Machine Learning
  • Deep Learning
  • Hybrid Models
  • Other Forecasting Methods

Data Inputs Covered:

  • Weather Data
  • Satellite Data
  • Crop Data
  • Soil Data
  • Field Sensor Data
  • Other Data Inputs

Forecast Outputs Covered:

  • Risk Scores
  • Disease Alerts
  • Outbreak Maps
  • Severity Forecasts
  • Treatment Timing
  • Other Forecast Outputs

End Users Covered:

  • Farmers
  • Agronomists
  • Crop Consultants
  • Agrochemical Companies
  • Government Agencies
  • Other End Users

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 Predictive Crop Health Market, By Disease

  • 5.1 Fungal Diseases
  • 5.2 Bacterial Diseases
  • 5.3 Viral Diseases
  • 5.4 Nematode Diseases
  • 5.5 Other Diseases

6 Global Predictive Crop Health Market, By Forecasting Method

  • 6.1 Weather Models
  • 6.2 Statistical Models
  • 6.3 Machine Learning
  • 6.4 Deep Learning
  • 6.5 Hybrid Models
  • 6.6 Other Forecasting Methods

7 Global Predictive Crop Health Market, By Data Input

  • 7.1 Weather Data
  • 7.2 Satellite Data
  • 7.3 Crop Data
  • 7.4 Soil Data
  • 7.5 Field Sensor Data
  • 7.6 Other Data Inputs

8 Global Predictive Crop Health Market, By Forecast Output

  • 8.1 Risk Scores
  • 8.2 Disease Alerts
  • 8.3 Outbreak Maps
  • 8.4 Severity Forecasts
  • 8.5 Treatment Timing
  • 8.6 Other Forecast Outputs

9 Global Predictive Crop Health Market, By End User

  • 9.1 Farmers
  • 9.2 Agronomists
  • 9.3 Crop Consultants
  • 9.4 Agrochemical Companies
  • 9.5 Government Agencies
  • 9.6 Other End Users

10 Global Predictive Crop Health Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Climate FieldView
  • 13.2 Plantix
  • 13.3 AgroStar
  • 13.4 Prospera Technologies Ltd.
  • 13.5 Arable Labs, Inc.
  • 13.6 Taranis Visual Ltd.
  • 13.7 PEAT GmbH
  • 13.8 PlantVillage
  • 13.9 Trace Genomics, Inc.
  • 13.10 OneSoil
  • 13.11 IBM Corporation
  • 13.12 John Deere
  • 13.13 Trimble Inc.
  • 13.14 AgriTask
  • 13.15 CropX Technologies Ltd.

List of Tables

  • Table 1 Global Predictive Crop Health Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Predictive Crop Health Market, By Disease (2023-2034) ($MN)
  • Table 3 Global Predictive Crop Health Market, By Fungal Diseases (2023-2034) ($MN)
  • Table 4 Global Predictive Crop Health Market, By Bacterial Diseases (2023-2034) ($MN)
  • Table 5 Global Predictive Crop Health Market, By Viral Diseases (2023-2034) ($MN)
  • Table 6 Global Predictive Crop Health Market, By Nematode Diseases (2023-2034) ($MN)
  • Table 7 Global Predictive Crop Health Market, By Other Diseases (2023-2034) ($MN)
  • Table 8 Global Predictive Crop Health Market, By Forecasting Method (2023-2034) ($MN)
  • Table 9 Global Predictive Crop Health Market, By Weather Models (2023-2034) ($MN)
  • Table 10 Global Predictive Crop Health Market, By Statistical Models (2023-2034) ($MN)
  • Table 11 Global Predictive Crop Health Market, By Machine Learning (2023-2034) ($MN)
  • Table 12 Global Predictive Crop Health Market, By Deep Learning (2023-2034) ($MN)
  • Table 13 Global Predictive Crop Health Market, By Hybrid Models (2023-2034) ($MN)
  • Table 14 Global Predictive Crop Health Market, By Other Forecasting Methods (2023-2034) ($MN)
  • Table 15 Global Predictive Crop Health Market, By Data Input (2023-2034) ($MN)
  • Table 16 Global Predictive Crop Health Market, By Weather Data (2023-2034) ($MN)
  • Table 17 Global Predictive Crop Health Market, By Satellite Data (2023-2034) ($MN)
  • Table 18 Global Predictive Crop Health Market, By Crop Data (2023-2034) ($MN)
  • Table 19 Global Predictive Crop Health Market, By Soil Data (2023-2034) ($MN)
  • Table 20 Global Predictive Crop Health Market, By Field Sensor Data (2023-2034) ($MN)
  • Table 21 Global Predictive Crop Health Market, By Other Data Inputs (2023-2034) ($MN)
  • Table 22 Global Predictive Crop Health Market, By Forecast Output (2023-2034) ($MN)
  • Table 23 Global Predictive Crop Health Market, By Risk Scores (2023-2034) ($MN)
  • Table 24 Global Predictive Crop Health Market, By Disease Alerts (2023-2034) ($MN)
  • Table 25 Global Predictive Crop Health Market, By Outbreak Maps (2023-2034) ($MN)
  • Table 26 Global Predictive Crop Health Market, By Severity Forecasts (2023-2034) ($MN)
  • Table 27 Global Predictive Crop Health Market, By Treatment Timing (2023-2034) ($MN)
  • Table 28 Global Predictive Crop Health Market, By Other Forecast Outputs (2023-2034) ($MN)
  • Table 29 Global Predictive Crop Health Market, By End User (2023-2034) ($MN)
  • Table 30 Global Predictive Crop Health Market, By Farmers (2023-2034) ($MN)
  • Table 31 Global Predictive Crop Health Market, By Agronomists (2023-2034) ($MN)
  • Table 32 Global Predictive Crop Health Market, By Crop Consultants (2023-2034) ($MN)
  • Table 33 Global Predictive Crop Health Market, By Agrochemical Companies (2023-2034) ($MN)
  • Table 34 Global Predictive Crop Health Market, By Government Agencies (2023-2034) ($MN)
  • Table 35 Global Predictive Crop Health Market, By Other End Users (2023-2034) ($MN)

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