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AIベースの害虫管理アプリの世界市場 - 2025年～2032年

Global AI Based Pest Management App Market - 2025-2032

出版日
ページ情報
英文 180 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
AIベースの害虫管理アプリの世界市場 - 2025年～2032年
出版日: 2025年03月25日
発行: DataM Intelligence
ページ情報: 英文 180 Pages
納期: 即日から翌営業日
GIIご利用のメリット
  • 全表示
  • 概要
  • 目次
概要

AIベースの害虫管理アプリの世界市場規模は2024年に24億9,523万米ドルに達し、2032年には83億6,268万米ドルに達すると予測され、予測期間2025-2032年のCAGRは16.32%で成長すると予測されます。

世界のAIベースの害虫管理アプリ市場は、農業、都市害虫駆除、食品安全におけるスマートでデータ駆動型の害虫駆除ソリューションに対する需要の高まりにより拡大しています。世界各国の政府は、作物保護を強化し、農薬の使用を最小限に抑え、病気を媒介する害虫の監視を改善するため、農業と公衆衛生におけるAIの導入を推進しています。国連食糧農業機関（FAO）によると、害虫は世界の作物の最大40％を毎年破壊し、2,200億米ドルの損失をもたらしています。

AIを搭載したアプリは、コンピューター・ビジョン、IoTセンサー、予測分析を使って害虫の発生を早期に発見し、化学農薬への依存を減らすのに役立ちます。米国農務省（USDA）は精密農業におけるAI主導の害虫駆除を支援し、欧州委員会のデジタル戦略はスマート農業ソリューションを推進しています。インド農業研究評議会（ICAR）は、主要作物の害虫検出におけるAI利用の増加を報告しています。アグリテックとAIへの投資が増加しており、市場は急成長を遂げようとしています。

世界のAIベースの害虫管理アプリ市場動向

精密農業に対する政府の支援

世界各国の政府は、害虫管理、作物保護、食糧安全保障を改善するため、AIを活用した精密農業を積極的に推進しています。米国農務省（USDA）は、農業研究局（ARS）のAIを活用した害虫モニタリングプログラムなどのイニシアチブを立ち上げ、農家が蔓延を早期に発見し、作物の損失と農薬の使用を削減するのを支援しています。

国連食糧農業機関（FAO）によると、害虫による被害は世界の農作物ロスの40％を占めており、スマートな害虫駆除ソリューションの必要性が強調されています。欧州委員会のFarm to Fork戦略は、2030年までに農薬使用量を50％削減することを目指しており、AIベースの害虫管理アプリの需要をさらに促進しています。

農村地域における限られたデジタルインフラ

AIベースの害虫管理アプリ市場にとっての大きな課題は、地方の農業地域におけるデジタルインフラの不足であり、AI主導のソリューションの採用を制限しています。多くの開発途上国、特にアフリカ、南アジア、ラテンアメリカでは、インターネット接続の不備、スマートフォンへのアクセス不足、農家のAIリテラシーの低さといった課題に直面しています。

世界銀行の報告によると、世界で29億人近くがインターネットにアクセスできず、農村部ではデジタルデバイドが最も大きいです。国連食糧農業機関（FAO）は、世界の食糧の30％以上を生産する零細農家が、必要な技術資源を持たないことが多いことを強調しています。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 精密農業に対する政府の支援
    • 抑制要因
      • 農村地域におけるデジタルインフラの限界
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • バリューチェーン分析
  • 価格分析
  • 規制およびコンプライアンス分析
  • AIと自動化の影響分析
  • 研究開発とイノベーション分析
  • 技術分析
  • DMIの見解

第6章 害虫の種類別

  • 昆虫
  • シロアリ
  • げっ歯類
  • その他

第7章 用途別

  • 農作物保護
  • 都市害虫駆除
  • 家畜保護
  • 保管製品保護
  • 林業害虫管理
  • その他

第8章 技術別

  • AIと機械学習
  • IoT対応害虫監視システム
  • コンピュータビジョンと画像認識
  • 害虫発生予測のための予測分析
  • 自動害虫駆除ソリューション
  • その他

第9章 エンドユーザー別

  • 独立栽培者
  • 商業農家
  • その他

第10章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他欧州地域
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋地域
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第11章 競合情勢

  • 競合シナリオ
  • 市況・シェア分析
  • M&A分析

第12章 企業プロファイル

  • Bayer AG
    • 会社概要
    • 製品ポートフォリオと概要
    • 財務概要
    • 主な発展
  • Syngenta AG
  • BASF SE
  • FMC Corporation
  • Taranis Inc.
  • PrecisionHawk Inc.
  • Rentokil Initial plc
  • Anticimex Group AB
  • DeepMind Technologies Limited
  • EcoPest Labs LLC

第13章 付録

目次
Product Code: ICT9354

Global AI Based Pest Management App Market size reached US$ 2,495.23 million in 2024 and is expected to reach US$ 8,362.68 million by 2032, growing with a CAGR of 16.32% during the forecast period 2025-2032.

The global AI-based pest management app market is expanding due to increasing demand for smart, data-driven pest control solutions in agriculture, urban pest control and food safety. Governments worldwide are promoting AI adoption in agriculture and public health to enhance crop protection, minimize pesticide use and improve monitoring of disease-carrying pests. According to the Food and Agriculture Organization (FAO), pests destroy up to 40% of global crops annually, causing $220 billion in losses.

AI-powered apps help detect pest infestations early using computer vision, IoT sensors and predictive analytics, reducing reliance on chemical pesticides. The U.S. Department of Agriculture (USDA) supports AI-driven pest control in precision farming, while the European Commission's Digital Strategy promotes smart agriculture solutions. The Indian Council of Agricultural Research (ICAR) reports increased AI use in pest detection for major crops. With rising investments in agritech and AI, the market is set for rapid growth.

Global AI Based Pest Management App Market Trends

Government Support for Precision Agriculture

Governments worldwide are actively promoting AI-driven precision agriculture to improve pest management, crop protection and food security. The U.S. Department of Agriculture (USDA) has launched initiatives such as the Agricultural Research Service (ARS) AI-driven pest monitoring programs, which help farmers detect infestations early, reducing crop losses and pesticide use.

According to the Food and Agriculture Organization (FAO), pest-related damage leads to 40% of global crop losses annually, emphasizing the need for smart pest control solutions. The European Commission's Farm to Fork Strategy aims to reduce pesticide use by 50% by 2030, further driving demand for AI-based pest management apps.

Limited Digital Infrastructure in Rural Farming Regions

A major challenge for the AI-based pest management app market is the lack of digital infrastructure in rural farming areas, limiting the adoption of AI-driven solutions. Many developing nations, particularly in Africa, South Asia and Latin America, face challenges such as poor internet connectivity, lack of smartphone access and limited AI literacy among farmers.

The World Bank reports that nearly 2.9 billion people worldwide lack internet access, with rural areas experiencing the highest digital divide. The Food and Agriculture Organization (FAO) highlights that smallholder farmers, who produce over 30% of global food, often lack the necessary technological resources.

Segment Analysis

The global AI based pest management app market is segmented based on pest type, application, technology, end-user and region.

AI-Powered Insect Pest Management: Government Initiatives and Global Demand

Insect pests pose a significant threat to global agriculture, leading to substantial crop losses annually. The Food and Agriculture Organization (FAO) estimates that plant pests and diseases account for the reduction of between 20 and 40 percent of global crop yields each year, contributing to food insecurity and economic losses. To address these challenges, there is a growing demand for advanced technologies, such as AI-based pest management applications, which offer precise monitoring and early detection of insect infestations, thereby enhancing crop protection strategies.

Additionally, the USDA's Agricultural Research Service (ARS) is exploring AI-based models for image-based identification of stored product insects, enhancing monitoring efficiency in grain facilities. These governmental efforts underscore a commitment to leveraging technology for sustainable agriculture, reflecting a broader trend towards precision agriculture and integrated pest management practices.

Geographical Penetration

Rapid Technological Advancements in North America.

The demand for AI-based pest management applications in North America is experiencing significant growth, driven by governmental initiatives and technological advancements. The U.S. Department of Agriculture (USDA) has recognized the potential of artificial intelligence (AI) in enhancing agricultural practices, including pest management. For instance, the USDA's National Institute of Food and Agriculture has invested over $7 million in research focusing on big data analytics, machine learning and AI to maintain the nation's leadership in food and agricultural production.

Additionally, projects like FACT-AI aim to develop AI-based decision support systems for pest identification in wheat production systems, facilitating more efficient and accurate pest management strategies. The Environmental Protection Agency (EPA) also promotes Integrated Pest Management (IPM) principles, emphasizing environmentally sensitive approaches that combine common-sense practices.

The integration of AI into IPM is being explored to enhance pest monitoring schemes, offering the potential for more effective and reliable warning systems for pest outbreaks. These governmental efforts underscore a commitment to incorporating advanced technologies like AI into pest management, reflecting a broader trend towards sustainable and efficient agricultural practices in North America.

Technology Analysis

The AI-based pest management market is undergoing rapid technological transformation, driven by advancements in automation, machine learning and data analytics. Smart pest control solutions leverage AI-powered sensors and computer vision to detect, identify and monitor pest populations in real-time, enhancing precision in pest management strategies. Machine learning in pest detection allows for pattern recognition and predictive analytics, enabling proactive pest control instead of reactive measures. The integration of automated pest control systems with Internet of Things (IoT) devices has improved remote pest monitoring, reducing the need for manual intervention.

Advanced pest monitoring technology is being deployed in agricultural fields through drones, image-based recognition systems and AI-driven traps that automatically analyze pest behavior. AI-powered applications use deep learning models to differentiate between harmful and beneficial insects, optimizing precision agriculture techniques. These solutions enable targeted pesticide application, significantly reducing chemical overuse and supporting sustainable pest management. Cloud-based AI platforms are further revolutionizing the industry by allowing real-time data sharing and predictive modeling, helping farmers and pest control operators make informed decisions.

Automated decision-making in pest management reduces operational costs by minimizing crop loss and labor expenses. The rise of AI-based pest management has also led to the development of smartphone-based pest identification apps, making advanced technology accessible to small-scale farmers. Robotics and AI-driven UAVs (unmanned aerial vehicles) are being deployed for large-scale pest surveillance, allowing for efficient monitoring across vast agricultural landscapes.

Competitive Landscape

The major global players in the market include Bayer AG, Syngenta AG, BASF SE, FMC Corporation, Taranis Inc., PrecisionHawk Inc., Rentokil Initial plc, Anticimex Group AB, DeepMind Technologies Limited and EcoPest Labs LLC.

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Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Pest Type
  • 3.2. Snippet by Application
  • 3.3. Snippet by Technology
  • 3.4. Snippet by End-User
  • 3.5. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Government Support for Precision Agriculture
    • 4.1.2. Restraints
      • 4.1.2.1. Limited Digital Infrastructure in Rural Farming Regions
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Value Chain Analysis
  • 5.4. Pricing Analysis
  • 5.5. Regulatory and Compliance Analysis
  • 5.6. AI & Automation Impact Analysis
  • 5.7. R&D and Innovation Analysis
  • 5.8. Technology Analysis
  • 5.9. DMI Opinion

6. By Pest Type

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Pest Type
    • 6.1.2. Market Attractiveness Index, By Pest Type
  • 6.2. Insects*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Termites
  • 6.4. Rodents
  • 6.5. Others

7. By Application

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.1.2. Market Attractiveness Index, By Application
  • 7.2. Crop Protection*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Urban Pest Control
  • 7.4. Livestock Protection
  • 7.5. Stored Product Protection
  • 7.6. Forestry Pest Management
  • 7.7. Others

8. By Technology

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 8.1.2. Market Attractiveness Index, By Technology
  • 8.2. AI and Machine Learning*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. IoT-Enabled Pest Monitoring Systems
  • 8.4. Computer Vision & Image Recognition
  • 8.5. Predictive Analytics for Pest Outbreak Forecasting
  • 8.6. Automated Pest Control Solutions
  • 8.7. Others

9. By End-User

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 9.1.2. Market Attractiveness Index, By End-User
  • 9.2. Independent Growers*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Commercial Farmers
  • 9.4. Others

10. By Region

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 10.1.2. Market Attractiveness Index, By Region
  • 10.2. North America
    • 10.2.1. Introduction
    • 10.2.2. Key Region-Specific Dynamics
    • 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Pest Type
    • 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.2.7.1. U.S.
      • 10.2.7.2. Canada
      • 10.2.7.3. Mexico
  • 10.3. Europe
    • 10.3.1. Introduction
    • 10.3.2. Key Region-Specific Dynamics
    • 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Pest Type
    • 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.3.7.1. Germany
      • 10.3.7.2. UK
      • 10.3.7.3. France
      • 10.3.7.4. Italy
      • 10.3.7.5. Russia
      • 10.3.7.6. Rest of Europe
  • 10.4. South America
    • 10.4.1. Introduction
    • 10.4.2. Key Region-Specific Dynamics
    • 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Pest Type
    • 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.4.7.1. Brazil
      • 10.4.7.2. Argentina
      • 10.4.7.3. Rest of South America
  • 10.5. Asia-Pacific
    • 10.5.1. Introduction
    • 10.5.2. Key Region-Specific Dynamics
    • 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Pest Type
    • 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.5.7.1. China
      • 10.5.7.2. India
      • 10.5.7.3. Japan
      • 10.5.7.4. Australia
      • 10.5.7.5. Rest of Asia-Pacific
  • 10.6. Middle East and Africa
    • 10.6.1. Introduction
    • 10.6.2. Key Region-Specific Dynamics
    • 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Pest Type
    • 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

  • 11.1. Competitive Scenario
  • 11.2. Market Positioning/Share Analysis
  • 11.3. Mergers and Acquisitions Analysis

12. Company Profiles

  • 12.1. Bayer AG*
    • 12.1.1. Company Overview
    • 12.1.2. Product Portfolio and Description
    • 12.1.3. Financial Overview
    • 12.1.4. Key Developments
  • 12.2. Syngenta AG
  • 12.3. BASF SE
  • 12.4. FMC Corporation
  • 12.5. Taranis Inc.
  • 12.6. PrecisionHawk Inc.
  • 12.7. Rentokil Initial plc
  • 12.8. Anticimex Group AB
  • 12.9. DeepMind Technologies Limited
  • 12.10. EcoPest Labs LLC

LIST NOT EXHAUSTIVE

13. Appendix

  • 13.1. About Us and Services
  • 13.2. Contact Us