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市场调查报告书
商品编码
1696217

全球基于人工智慧的害虫管理应用市场 - 2025 年至 2032 年

Global AI Based Pest Management App Market - 2025-2032
出版日期: | 出版商: DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2个工作天内

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简介目录

2024 年全球基于人工智慧的害虫管理应用程式市场规模达到 24.9523 亿美元，预计到 2032 年将达到 83.6268 亿美元，在 2025-2032 年预测期内的复合年增长率为 16.32%。

由于农业、城市虫害控制和食品安全领域对智慧、数据驱动的虫害控制解决方案的需求不断增加，全球基于人工智慧的虫害管理应用程式市场正在扩大。世界各国政府正在推动在农业和公共卫生领域采用人工智慧，以加强作物保护、减少农药使用并改善对携带疾病的害虫的监测。根据粮食及农业组织（FAO）统计，害虫每年摧毁全球高达 40% 的农作物，造成 2,200 亿美元的损失。

人工智慧应用程式利用电脑视觉、物联网感测器和预测分析来帮助及早发现害虫的侵扰，减少对化学农药的依赖。美国农业部（USDA）支持精准农业中人工智慧驱动的害虫防治，而欧盟委员会的数位策略则推动智慧农业解决方案。印度农业研究理事会 (ICAR) 报告称，人工智慧在主要农作物害虫检测中的应用有所增加。随着对农业科技和人工智慧的投资不断增加，市场将迎来快速成长。

全球基于人工智慧的害虫管理应用市场趋势

政府支持精准农业

世界各国政府正积极推动人工智慧驱动的精准农业，以改善害虫管理、作物保护和粮食安全。美国农业部（USDA）已启动农业研究服务局（ARS）人工智慧驱动的害虫监测计画等倡议，帮助农民及早发现虫害，减少作物损失和农药使用。

根据粮食及农业组织 (FAO) 的数据，与害虫相关的损害每年导致全球 40% 的农作物损失，凸显了对智慧害虫防治解决方案的需求。欧盟委员会的「从农场到餐桌」策略旨在到 2030 年减少 50% 的农药使用量，这进一步推动了基于人工智慧的害虫管理应用程式的需求。

农村农业地区数位基础设施有限

基于人工智慧的害虫管理应用程式市场面临的一个主要挑战是农村农业地区缺乏数位基础设施，限制了人工智慧驱动解决方案的采用。许多发展中国家，特别是非洲、南亚和拉丁美洲的国家，面临网路连结性差、智慧型手机普及率低以及农民人工智慧知识有限等挑战。

世界银行报告称，全球有近29亿人无法上网，农村地区的数位落差最为严重。粮食及农业组织（FAO）强调，生产了全球30%以上粮食的小农户往往缺乏必要的技术资源。

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

  • 影响因素
    • 驱动程式
      • 政府支持精准农业
    • 限制
      • 农村农业地区数位基础设施有限
    • 机会
    • 影响分析

第五章：产业分析

  • 波特五力分析
  • 供应链分析
  • 价值链分析
  • 定价分析
  • 监理与合规性分析
  • 人工智慧与自动化影响分析
  • 研发与创新分析
  • 技术分析
  • DMI 意见

第 6 章：按害虫类型

  • 昆虫
  • 白蚁
  • 囓齿动物
  • 其他的

第 7 章：按应用

  • 农作物保护
  • 城市害虫防治
  • 牲畜保护
  • 储存产品保护
  • 林业病虫害防治
  • 其他的

第 8 章：按技术

  • 人工智慧和机器学习
  • 物联网害虫监测系统
  • 电脑视觉与影像识别
  • 害虫爆发预测分析
  • 自动化害虫防治解决方案
  • 其他的

第 9 章：按最终用户

  • 独立种植者
  • 商业农民
  • 其他的

第 10 章：按地区

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 俄罗斯
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 亚太其他地区
  • 中东和非洲

第 11 章：竞争格局

  • 竞争格局
  • 市场定位/份额分析
  • 併购分析

第 12 章：公司简介

  • Bayer AG
    • 公司概况
    • 产品组合和描述
    • 财务概览
    • 关键进展
  • 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：附录

简介目录
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