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市场调查报告书
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1859794

全球基于人工智慧的作物病害检测市场：预测至2032年－按组件、病害类型、作物类型、技术、应用、最终用户和地区进行分析

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个工作天内

价格

据 Stratistics MRC 称，全球人工智慧作物病害检测市场预计到 2025 年将达到 16 亿美元，到 2032 年将达到 59 亿美元，预测期内复合年增长率为 19.5%。

人工智慧驱动的作物病害检测技术结合了电脑视觉、机器学习以及来自卫星、无人机和接近感测器的图像，能够识别病害、虫害和营养压力的早期症状。自动化诊断有助于精准防治，减少作物损失，优化化学品使用，并实现更永续的干预措施。随着模型的改进、感测器的普及以及与农场管理平台的集成，市场应用将会不断扩大。

根据《国际工程与技术研究期刊》(IRJET) 的报导，利用影像处理和机器学习的人工智慧作物病害检测技术，在辨识小麦和水稻的叶枯病和銹病方面，准确率高达 92%。

加强粮食安全的必要性

全球人口成长和气候压力加剧了对可靠作物产量的需求，使得人工智慧驱动的病害检测至关重要。农民、相关企业和政策制定者都在优先考虑能够早期识别病原体的技术，以减少损失并提高粮食供应。此外，早期检测还能减少化学投入，进而实现永续生产并降低成本。公共和私人对精密农业的投资将加速相关研究、部署和规模化推广。这将促使更多商业农场和寻求建构弹性供应链的合作模式采用精准农业技术，同时提升全球农民的决策能力。

技术意识有限

人工智慧疾病检测工具的普及应用受到许多农民技术素养低下和推广支援不足的限制。小农户可能缺乏智慧型手机、可靠的网路连接，或缺乏遵循自动化建议的信心，这限制了这些工具的实际效用。供应商在提供培训、本地化介面和持续支援方面面临更高的成本。此外，持怀疑态度的相关人员可能会抵制以数据主导的传统耕作方式变革。要克服这些限制，需要进行有针对性的能力建设，与当地农业机构合作，采用以用户为中心的设计，并部署价格合理、互联互通的解决方案，以确保这些工具能够得到切实有效的应用和持续推广。

与农场管理软体集成

将人工智慧病害检测模组嵌入农场管理系统，可将诊断结果与日程安排、投入品采购和记录保存关联起来，从而提升价值。农民能够收到基于上下文的建议，这些建议会将警报转化为可执行的任务，例如有针对性地喷洒农药或调整灌溉方式。这种整合简化了工作流程，提高了买方的可追溯性，并支援认证系统。此外，此整合平台能够提供丰富的资料集来优化模型，从而形成回馈循环，提高准确性。对于供应商而言，这种整合能够带来订阅收入、交叉销售，并有助于与世界各地的农业相关企业和合作社建立更深入的企业合作关係。

资料隐私和安全问题

收集田间影像、感测器资料流和管理记录会产生高度敏感的资料集，如果处理不当，可能会损害人们对人工智慧作物监测服务的信任。农民担心未授权存取、作物资讯的商业性用途以及衍生模型的所有权不明。不同司法管辖区的监管规定各不相同，这增加了国际供应商的合规负担。此外，资料外洩和模型中毒等网路风险也可能扰乱营运。

新冠疫情的影响：

疫情凸显了远端自动化作物监测的价值，因为旅行限制和劳动力短缺限制了田间作业。儘管短期内部署有所延迟，但持续的投资转向了人工智慧工具，这些工具减少了实地考察次数，并实现了连续监测。供应链的压力增加了对早期检测以保护产量的需求，而公共资金和研究伙伴关係则支持了试点计画。整体而言，新冠疫情加速了数位农业的普及，并展现了数位农业在增强大小农场韧性方面的重要作用。

预计在预测期内，真菌病害细分市场将是最大的细分市场。

预计在预测期内，真菌病害领域将占据最大的市场份额。銹病、霜霉病和晚疫病等病害对谷物、水果和蔬菜的产量造成了重大损失，因此对可靠诊断方法的需求持续旺盛。能够检测早期症状的人工智慧解决方案可以减少活性化学品的使用，并提高作物品质。与应用平台和咨询服务的整合进一步提升了投资报酬率。随着资料集在不同地区的扩展，模型准确性不断提高，这使得全球供应商提供的真菌检测产品更受青睐。

预计在预测期内，软体产业将实现最高的复合年增长率。

预计在预测期内，软体产业将呈现最高的成长率。可扩展性、快速部署和持续学习循环使软体对各种规模和地理的农场都极具吸引力。 SaaS 定价和云端原生架构降低了前期投资，并有助于从试验和试点阶段过渡到规模化生产。与感测器和无人机的互通性提高了软体的实用性，而利用新的田间资料定期重新训练模型则提高了在当地条件下的侦测能力。由于农业技术投资者青睐轻资产平台，资本流动和伙伴关係有助于产品改进、市场拓展以及加速软体主导解决方案的普及应用。

占比最大的地区：

预计北美将在预测期内占据最大的市场份额。完善的农业技术生态系统、广泛的互联互通以及高度机械化，为人工智慧检测平台的快速部署提供了有力支撑。大型商业农场和精密农业服务供应商正大力投资先进的感测、分析和决策支援工具，从而创造了巨大的市场需求。此外，强劲的私人投资、研究机构以及农业相关企业和商品买家有利的采购预算，也推动了供应商的创新。清晰的监管环境和完善的数据基础设施，进一步促进了全部区域的规模部署和商业性伙伴关係。

复合年增长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率。农业的快速数位化、智慧型手机普及率的不断提高以及政府对精密农业的支持计划，为人工智慧疾病检测技术的普及创造了有利条件。庞大的小农户群体为低成本、行动优先的解决方案提供了可扩展性，而本地新兴企业正在根据当地作物和语言调整其模式。国际供应商正与经销商和研究伙伴关係合作，以实现产品在地化。随着基础设施的改善和农业技术投资的增加，预计全部区域的普及率将加速提升。

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目录

第一章执行摘要

第二章 引言

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

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球人工智慧作物病害检测市场（按组件划分）

  • 硬体
    • 相机
    • 无人机/无人飞行器
    • 智慧型手机和平板电脑
    • 加工设备和感测器
  • 软体
    • 人工智慧/机器学习平台
    • 行动应用
    • 其他软体
  • 服务
    • 整合和部署
    • 支援和维护
    • 咨询和培训

6. 全球人工智慧作物病害检测市场（依病害类型划分）

  • 真菌病
  • 细菌性疾病
  • 病毒性疾病
  • 虫害
  • 营养缺乏

7. 全球人工智慧作物病害检测市场（依作物类型划分）

  • 粮食
  • 水果和蔬菜
  • 油籽/豆类
  • 经济作物
  • 其他作物

8. 全球人工智慧作物病害检测市场（依技术划分）

  • 机器学习/深度学习
    • 卷积类神经网路（CNN）
    • 循环神经网路（RNN）
    • 迁移学习
  • 电脑视觉
  • 预测分析
  • 自然语言处理

9. 全球人工智慧作物病害检测市场（按应用领域划分）

  • 农田监测/勘察
  • 品质评价和产量监测
  • 农场层面建议、治疗方案
  • 研究与开发

第十章 全球人工智慧作物病害检测市场（依最终用户划分）

  • 个体/小农户
  • 大型企业农场和农业相关企业
  • 政府机构和研究机构
  • 农业合作社

第十一章 全球人工智慧作物病害检测市场（按地区划分）

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

第十二章：主要趋势

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

第十三章：企业概况

  • 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.