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1696217

全球基於人工智慧的害蟲管理應用市場 - 2025 年至 2032 年

Global AI Based Pest Management App Market - 2025-2032

出版日期: 2025年03月25日 | 出版商:

DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球基於人工智慧的害蟲管理應用程式市場規模達到 24.9523 億美元，預計到 2032 年將達到 83.6268 億美元，在 2025-2032 年預測期內的複合年成長率為 16.32%。

由於農業、城市蟲害控制和食品安全領域對智慧、數據驅動的蟲害控制解決方案的需求不斷增加，全球基於人工智慧的蟲害管理應用程式市場正在擴大。世界各國政府正在推動在農業和公共衛生領域採用人工智慧，以加強作物保護、減少農藥使用並改善對攜帶疾病的害蟲的監測。根據糧食及農業組織（FAO）統計，害蟲每年摧毀全球高達 40% 的農作物，造成 2,200 億美元的損失。

人工智慧應用程式利用電腦視覺、物聯網感測器和預測分析來幫助及早發現害蟲的侵擾，減少對化學農藥的依賴。美國農業部（USDA）支持精準農業中人工智慧驅動的害蟲防治，而歐盟委員會的數位策略則推動智慧農業解決方案。印度農業研究理事會 (ICAR) 報告稱，人工智慧在主要農作物害蟲檢測中的應用有所增加。隨著對農業科技和人工智慧的投資不斷增加，市場將迎來快速成長。

全球基於人工智慧的害蟲管理應用市場趨勢

政府支持精準農業

世界各國政府正積極推動人工智慧驅動的精準農業，以改善害蟲管理、作物保護和糧食安全。美國農業部（USDA）已啟動農業研究服務局（ARS）人工智慧驅動的害蟲監測計畫等舉措，幫助農民及早發現蟲害，減少作物損失和農藥使用。

根據糧食及農業組織 (FAO) 的數據，與害蟲相關的損害每年導致全球 40% 的農作物損失，凸顯了對智慧害蟲防治解決方案的需求。歐盟委員會的「從農場到餐桌」策略旨在到 2030 年減少 50% 的農藥使用量，這進一步推動了基於人工智慧的害蟲管理應用程式的需求。

農村農業地區數位基礎設施有限

基於人工智慧的害蟲管理應用程式市場面臨的一個主要挑戰是農村農業地區缺乏數位基礎設施，限制了人工智慧驅動解決方案的採用。許多發展中國家，特別是非洲、南亞和拉丁美洲的國家，面臨網路連結性差、智慧型手機普及率低以及農民人工智慧知識有限等挑戰。

世界銀行報告稱，全球有近29億人無法上網，農村地區的數位落差最為嚴重。糧食及農業組織（FAO）強調，生產了全球30%以上糧食的小農戶往往缺乏必要的技術資源。

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

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