한글목차

세계 농업 분석 시장의 미래는 농부, 농학자, 농업 관련 기업, 농업 연구자, 정부 기관 등 다양한 시장에서의 기회로 인해 유망합니다. 세계 농업 분석 시장은 2025년부터 2031년까지 12.7%의 CAGR로 2031년까지 약 37억 달러에 달할 것으로 예상됩니다. 이 시장의 주요 촉진요인은 최적의 자원 활용에 대한 요구 증가, 개선된 농업 기술 개발을 위한 정부 이니셔티브 증가, IoT 기기 채택 증가 등입니다.

• Lucintel의 예측에 따르면, 농업 유형별로는 축산업이 예측 기간 동안 가장 높은 성장세를 보일 것으로 예상됩니다.
• 최종 용도별로는 농학자가 가장 높은 성장세를 보일 것으로 예상됩니다.
• 지역별로는 북미가 농업 생산량 증가와 디지털 농업 기술에 대한 투자 확대로 인해 예측 기간 동안 가장 큰 지역으로 남을 것으로 보입니다.

농업 분석 시장의 전략적 성장 기회

농업 분석 시장은 다양한 응용 분야에서 데이터 기반 기술의 채택이 증가함에 따라 성장의 원동력이 되고 있습니다. 세계 식량 수요와 지속가능성 목표를 충족하기 위해 농업 관행이 진화함에 따라 몇 가지 전략적 성장 기회가 등장하고 있습니다. 이러한 기회는 농업 분석의 다양한 애플리케이션에 걸쳐 있으며, 혁신과 확장 가능성을 제공합니다. 주요 애플리케이션에 집중함으로써 기업과 이해관계자들은 이러한 기회를 활용하여 농업 부문의 생산성, 효율성 및 지속가능성을 높일 수 있습니다.

• 정밀 농업 : 정밀 농업은 GPS 기술, 센서, 데이터 분석의 발전으로 농업 분석 시장의 주요 성장 기회로 떠오르고 있습니다. 이 애플리케이션은 물, 비료, 농약과 같은 작물 투입물을 특정 밭 조건에 맞게 정확하게 관리할 수 있게 해줍니다. 정밀 농업은 자원 활용을 최적화하고 낭비를 최소화함으로써 작물 수확량을 늘리고 환경에 미치는 영향을 줄일 수 있습니다. 지속가능한 농법과 자원 관리의 효율성에 대한 수요가 증가함에 따라 정밀 농업 솔루션의 확대가 가속화되고 있습니다.
• 작물 관리 및 수확량 예측 : 작물 관리 및 수확량 예측을 위한 고급 분석은 주요 성장 분야입니다. 위성 이미지, 기상 예측, 과거 작물 실적 데이터를 활용하여 작물의 수확량과 건강 상태를 정확하게 예측할 수 있는 도구를 제공합니다. 이를 통해 농부들은 작물 재배, 수확, 자원 배분에 대한 정보에 입각한 의사결정을 내릴 수 있습니다. 작물의 성과를 예측할 수 있는 능력은 농업 계획을 최적화하고 수확량 변동에 따른 위험을 줄이는 데 도움이 되며, 전체 농장의 생산성과 수익성을 지원합니다.
• 토양 건강 모니터링 : 토양 건강 모니터링은 농업 분석의 중요한 응용 분야로 인기를 끌고 있습니다. 영양분 수준, 수분 함량, pH와 같은 토양 특성을 분석하는 기술은 토양 관리를 위한 귀중한 인사이트를 제공합니다. 이 애플리케이션은 농부들이 최적의 토양 조건을 유지하고, 작물 생육을 개선하며, 장기적으로 토양 비옥도를 유지하는 데 도움이 됩니다. 토양 건강이 농업의 지속가능성에 미치는 영향에 대한 인식이 높아짐에 따라 토양 모니터링 솔루션에 대한 투자가 증가함에 따라 이 분야에 큰 성장 기회를 제공할 것으로 예상됩니다.
• 가축 모니터링 : 가축 모니터링은 가축의 건강과 생산성 향상에 초점을 맞춘 농업 분석의 새로운 응용 분야입니다. 웨어러블 센서, GPS 추적, 데이터 분석을 사용하는 이 솔루션은 동물의 행동, 건강 상태, 생산성에 대한 실시간 인사이트를 제공합니다. 이 애플리케이션을 통해 농가는 문제를 조기에 발견하고, 사료 공급 방법을 최적화하며, 전체 가축 관리를 강화할 수 있습니다. 동물 복지와 효율적인 가축 관리에 대한 관심이 높아지면서 첨단 가축 모니터링 기술에 대한 수요가 증가하고 있습니다.
• 공급망 최적화 : 공급망 최적화는 농업 분석의 전략적 성장 기회입니다. 농장에서 식탁까지 농산물의 이동을 추적하고 분석하는 데이터 분석 플랫폼은 업무 간소화, 낭비 감소, 추적성 향상에 도움이 됩니다. 이러한 솔루션은 공급망 관리를 강화함으로써 더 나은 의사결정을 지원하고, 보다 효율적이고 지속가능한 농업 공급망에 기여합니다.

농업 분석의 전략적 성장 기회는 정밀성, 효율성 및 지속가능성을 향상시켜 농업 부문의 미래를 형성합니다. 정밀 농업, 작물 관리, 토양 건강 모니터링, 가축 모니터링, 공급망 최적화 등의 응용 분야는 혁신과 성장을 촉진할 것입니다. 이러한 기회가 계속 발전함에 따라 농업 관행에 큰 영향을 미치고 생산성 향상과 농업에 대한 보다 지속가능한 접근 방식에 기여할 것으로 예상됩니다.

농업 분석 시장의 촉진요인 및 과제

농업 분석 시장은 시장 개척을 형성하는 다양한 촉진요인 및 과제에 영향을 받고 있습니다. 기술적 진보, 경제적 요인, 규제에 대한 고려는 시장 성장을 촉진하고 장애물을 해결하는 데 중요한 역할을 합니다. 이해관계자들이 시장을 효과적으로 탐색하고 위험을 줄이면서 성장의 기회를 활용하기 위해서는 이러한 촉진요인과 도전 과제를 이해하는 것이 필수적입니다.

농업 분석 시장을 이끄는 요인은 다음과 같습니다:

• 기술 발전 : 센서, 데이터 분석, AI의 급속한 기술 발전이 농업 분석의 성장을 촉진합니다. 토양 및 작물 모니터링을 위한 첨단 센서, 예측 분석을 위한 머신러닝 알고리즘, 항공 촬영을 위한 드론과 같은 기술 혁신은 데이터 수집 및 분석을 강화합니다. 이러한 기술은 정밀 농업을 가능하게 하고, 자원 관리를 개선하며, 작물 수확량을 최적화합니다. 기술의 지속적인 발전은 새로운 기능과 효율성을 제공하여 농업 분석 시장을 발전시킬 것입니다.
• 식량 안보에 대한 수요 증가 : 세계 인구 증가와 식량 수요 증가는 농업 분석 시장의 중요한 촉진요인입니다. 이러한 수요에 대응하기 위해서는 농업의 생산성과 효율성을 높여야 합니다. 농업 분석은 작물 수확량을 최적화하고, 자원을 효과적으로 관리하며, 식량 안보를 보장하는 데 도움이 되는 도구와 인사이트를 제공합니다. 더 많은 식량을 생산해야 한다는 압박이 가중되는 가운데, 지속가능한 농업을 실현하기 위해서는 데이터 기반 솔루션의 채택이 필수적입니다.
• 지속가능성 중시 : 지속가능성은 농업 분석의 주요 촉진요인으로, 환경에 미치는 영향을 줄이고 친환경 농법을 촉진하는 데 중점을 두고 있습니다. 정밀 농업, 토양 건강 모니터링, 자원 최적화를 지원하는 분석 솔루션은 폐기물을 최소화하고 자원을 절약함으로써 지속가능성 목표에 부합합니다. 환경 문제에 대한 인식이 높아지고 지속가능한 관행을 채택하라는 규제 당국의 압력으로 인해 환경 친화적인 농업 부문에 기여하는 분석 솔루션에 대한 수요가 증가하고 있습니다.
• 정부의 지원과 투자 : 농업 기술 및 데이터 분석에 대한 정부의 노력과 투자는 시장 성장을 촉진합니다. 많은 정부들이 농업에 첨단 기술을 도입할 수 있도록 정책을 시행하고 자금을 지원하고 있습니다. 이러한 투자는 농업 분석 솔루션의 개발 및 배포를 가속화하고, 농부들이 보다 쉽게 이용할 수 있도록 하며, 전체 시장의 성장을 촉진합니다.
• 사물인터넷(IoT) 및 빅데이터 도입 확대 : 사물인터넷(IoT) 장치와 빅데이터 분석의 도입은 농업의 혁신을 촉진하고, IoT 센서는 농업 작업의 다양한 측면에 대한 실시간 데이터를 제공하고, 빅데이터 분석 플랫폼은 의사결정을 위한 인사이트를 제공합니다. 인사이트를 제공합니다. 이러한 기술을 통합함으로써 농부들은 보다 효율적으로 작업을 모니터링하고 관리할 수 있어 효율성과 생산성을 향상시킬 수 있으며, IoT와 빅데이터의 보급은 농업 분석의 성장에 큰 기여를 하고 있습니다.

농업 분석 시장의 과제는 다음과 같습니다:

• 높은 도입 비용 : 농업 분석 시장의 주요 과제 중 하나는 첨단 기술의 도입 비용이 높다는 점입니다. 센서, 데이터 분석 플랫폼 및 기타 기술에 필요한 초기 투자비용은 중소규모의 농장에 있어서는 엄청난 금액이 될 수 있습니다. 높은 도입 비용은 농업 분석 솔루션의 채택을 제한하고, 일부 이해관계자에게는 진입장벽이 되어 전체 시장 성장에 영향을 미칠 수 있습니다.
• 데이터 프라이버시 및 보안 문제 : 데이터 프라이버시 및 보안 문제는 농업 분석 시장에서 중요한 과제입니다. 농업 분석은 기밀 데이터의 수집 및 분석을 포함하기 때문에 데이터 침해 및 악용의 위험이 있습니다. 강력한 데이터 보호 조치를 보장하고 프라이버시 문제를 해결하는 것은 신뢰를 유지하고 분석 솔루션의 채택을 촉진하는 데 있어 매우 중요합니다. 엄격한 보안 프로토콜의 필요성은 시장에서 지속적으로 제기되고 있는 과제입니다.
• 개발도상국의 기술 접근성 제한 : 신흥국 시장에서는 첨단 기술 및 인프라에 대한 접근이 제한적이라는 점이 농업 분석 시장에 대한 도전과제로 작용하고 있습니다. 기술 인프라가 부족한 지역에서는 데이터 기반 솔루션 도입이 늦어질 수 있습니다. 기술 격차를 해소하고 합리적인 가격의 솔루션을 제공하는 것은 농업 분석의 보급을 확대하고 그 이점을 전 세계적으로 실현하는 데 필수적입니다.

농업 분석 시장은 기술 발전, 식량 안보 수요, 지속가능성 목표, 정부 지원, IoT 및 빅데이터 도입 등의 영향을 크게 받고 있습니다. 그러나 높은 도입 비용, 데이터 프라이버시에 대한 우려, 신흥국 지역의 기술 접근성 제한 등의 문제도 시장에 영향을 미치고 있습니다. 농업 분석이 지속적으로 성장하고 발전하여 생산성 향상과 지속가능성이라는 목표를 달성하기 위해서는 이러한 촉진요인과 과제를 해결하는 것이 매우 중요합니다.

목차

제1장 주요 요약

제2장 세계의 농업 분석 시장 : 시장 역학

• 소개, 배경, 분류
• 공급망
• 업계 성장 촉진요인과 과제

제3장 2019년부터 2031년까지 시장 동향과 예측 분석

• 거시경제 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 농업 분석 시장 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 농업 분석 시장 : 농업 유형별
  • 정밀 농업
  • 축산
  • 양식업
  • 수직농법
  • 기타
• 세계의 농업 분석 시장 : 농장 규모별
  • 대규모 농장
  • 중소규모 농장
• 세계의 농업 분석 시장 : 기술별
  • 원격탐사와 위성 영상
  • 지리정보 시스템
  • 로봇공학과 자동화
  • 빅데이터와 클라우드 컴퓨팅
  • 시각화와 보고서
  • 블록체인 기술
  • 기타
• 세계의 농업 분석 시장 : 최종 용도별
  • 농가
  • 농학자
  • 농업 관련 산업
  • 농업 연구자
  • 정부기관
  • 기타

제4장 2019년부터 2031년까지 지역별 시장 동향과 예측 분석

• 지역별 : 세계의 농업 분석 시장
• 북미의 농업 분석 시장
• 유럽의 농업 분석 시장
• 아시아태평양의 농업 분석 시장
• 기타 지역의 농업 분석 시장

제5장 경쟁 분석

• 제품 포트폴리오 분석
• 업무 통합
• Porter's Five Forces 분석

제6장 성장 기회와 전략 분석

• 성장 기회 분석
  • 세계의 농업 분석 시장 성장 기회 : 농업 유형별
  • 세계의 농업 분석 시장 성장 기회 : 농장 규모별
  • 세계의 농업 분석 시장 성장 기회 : 기술별
  • 세계의 농업 분석 시장 성장 기회 : 최종 용도별
  • 세계의 농업 분석 시장 성장 기회 : 지역별
• 세계의 농업 분석 시장 최신 동향
• 전략 분석
  • 신제품 개발
  • 세계의 농업 분석 시장 능력 확대
  • 세계의 농업 분석 시장 합병, 인수, 합작투자
  • 인증과 라이선싱

제7장 주요 기업 개요

• Deere & Company
• IBM
• Bayer
• SAP
• Trimble
• Accenture
• ABACO
• DeLaval
• Oracle
• DTN

영문목차

The future of the global agriculture analytic market looks promising with opportunities in the farmer, agronomist, agribusiness, agricultural researcher, and government agency markets. The global agriculture analytic market is expected to reach an estimated $3.7 billion by 2031 with a CAGR of 12.7% from 2025 to 2031. The major drivers for this market are the growing need for optimal resource utilization, rising government initiatives for deploying improved agricultural techniques, and the rise in the adoption of IoT devices.

• Lucintel forecasts that, Within the agriculture type category, livestock farming is expected to witness the highest growth over the forecast period.
• Within the end use category, agronomist is expected to witness the highest growth.
• In terms of regions, North America will remain the largest region over the forecast period due to expanding the region's agricultural output and investing more in digital agriculture techniques.

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Emerging Trends in the Agriculture Analytic Market

The agriculture analytics market is witnessing several emerging trends that are reshaping how data is used in farming. These trends reflect advancements in technology and changing priorities within the agricultural sector, from enhancing precision to promoting sustainability. As technology continues to evolve, these trends drive significant transformations in how agricultural data is collected, analyzed, and applied, leading to more efficient and sustainable farming practices.

• AI and Machine Learning Integration: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into agriculture analytics, offering advanced predictive capabilities and automated decision-making. AI algorithms analyze vast amounts of data from various sources, such as satellite imagery and sensors, to provide actionable insights. This trend helps farmers optimize crop management, predict yields, and detect issues like pest infestations early. The result is more precise and data-driven farming practices that significantly enhance productivity and reduce waste.
• Precision Agriculture Technologies: Precision agriculture technologies are transforming farming by enabling more accurate and efficient use of resources. Technologies such as GPS-guided tractors, variable rate application systems, and remote sensing tools allow farmers to apply inputs like water, fertilizers, and pesticides precisely where needed. This trend reduces the overuse of resources, minimizes environmental impact, and increases crop yields. By leveraging detailed data and advanced analytics, precision agriculture supports sustainable and efficient farming practices.
• IoT and Smart Farming Solutions: The Internet of Things (IoT) plays a crucial role in agriculture analytics by connecting various sensors and devices to collect real-time data from the field. Smart farming solutions utilize this data to monitor soil conditions, weather patterns, and crop health. IoT-enabled devices provide continuous feedback and automation, helping farmers make informed decisions and respond quickly to changing conditions. This trend enhances operational efficiency and supports precision agriculture practices.
• Blockchain for Traceability and Transparency: Blockchain technology is gaining traction in agriculture analytics for its ability to enhance traceability and transparency in the supply chain. By providing a secure and immutable record of transactions, blockchain helps track the origin and journey of agricultural products from farm to table. This trend improves food safety, reduces fraud, and builds consumer trust. It also supports better management of resources and adherence to quality standards.
• Sustainable and Regenerative Farming Analytics: There is a growing emphasis on sustainable and regenerative farming practices supported by advanced analytics. Tools that analyze soil health, biodiversity, and environmental impact are becoming essential for promoting sustainable agriculture. Analytics help farmers implement practices that improve soil fertility, reduce carbon footprints, and enhance ecosystem health. This trend aligns with global efforts to address climate change and promote long-term agricultural sustainability.

These emerging trends significantly reshape the agriculture analytics market by driving innovation and enhancing farming practices. AI, precision agriculture, IoT, blockchain, and sustainability-focused analytics transform how data is used to improve productivity, efficiency, and sustainability in agriculture. As these trends continue to evolve, they promise to address critical challenges and unlock new opportunities for the agricultural sector.

Recent Developments in the Agriculture Analytic Market

The agriculture analytics market reflects the sector's ongoing transformation driven by technological innovation and a focus on optimizing agricultural practices. These advancements make data-driven approaches more accessible and effective, addressing key challenges such as resource management, productivity, and sustainability. By integrating advanced technologies, the agriculture analytics market evolves rapidly, offering new solutions and insights to enhance farming practices across the globe.

• Advanced Sensor Technologies: Recent advancements in sensor technologies have revolutionized data collection in agriculture. Modern sensors, including those for soil moisture, nutrient levels, and weather conditions, provide real-time data that helps farmers make informed decisions. These sensors are increasingly integrated into precision farming systems, allowing for better management of resources and improved crop health monitoring. The enhanced accuracy and timeliness of data support more effective and targeted agricultural practices.
• Big Data and Analytics Platforms: The rise of big data and advanced analytics platforms transforms agriculture by providing deeper insights into farming operations. These platforms analyze large volumes of data from various sources, such as satellites and IoT devices, to offer comprehensive insights into crop performance, soil conditions, and market trends. This development enables farmers to optimize their operations, forecast yields, and make data-driven decisions that enhance productivity and efficiency.
• Autonomous Farming Equipment: Autonomous farming equipment, including drones and self-driving tractors, is becoming increasingly prevalent. These technologies automate tasks such as planting, harvesting, and monitoring crops, reducing the need for manual labor and increasing operational efficiency. Autonomous equipment leverages data from sensors and AI to perform tasks with high precision, leading to more consistent and reliable farming practices.
• Cloud-Based Data Management: Cloud-based data management solutions facilitate the storage, processing, and analysis of agricultural data. These platforms offer scalable and accessible solutions for managing vast amounts of data collected from various sources. Cloud-based systems enable farmers to access real-time insights and collaborate with other stakeholders more effectively. This development supports more efficient data management and enhances decision-making capabilities.
• Integration of Blockchain for Supply Chain Transparency: Blockchain technology is integrated into agriculture analytics to improve supply chain transparency and traceability. By providing a secure and immutable record of transactions, blockchain enhances the ability to track the origin and journey of agricultural products. This development promotes food safety, reduces fraud, and ensures compliance with quality standards, benefiting both producers and consumers.

Recent developments in agriculture analytics significantly impact the sector by introducing advanced technologies and data-driven solutions. The adoption of sensor technologies, big data platforms, autonomous equipment, cloud-based systems, and blockchain enhances productivity, efficiency, and transparency in agriculture. These developments reshape farming practices and address key challenges, paving the way for a more innovative and sustainable agricultural future.

Strategic Growth Opportunities for Agriculture Analytic Market

The agriculture analytics market witnesses significant growth driven by the increasing adoption of data-driven technologies across various applications. As agricultural practices evolve to meet global food demands and sustainability goals, several strategic growth opportunities emerge. These opportunities span different applications within agriculture analytics, offering the potential for innovation and expansion. By focusing on key applications, businesses, and stakeholders can leverage these opportunities to enhance productivity, efficiency, and sustainability in the agricultural sector.

• Precision Agriculture: Precision agriculture is a major growth opportunity within the agriculture analytics market, driven by advancements in GPS technology, sensors, and data analytics. This application allows for the precise management of crop inputs such as water, fertilizers, and pesticides, tailored to specific field conditions. By optimizing resource use and minimizing waste, precision agriculture enhances crop yields and reduces environmental impact. The growing demand for sustainable farming practices and increased efficiency in resource management fuels the expansion of precision agriculture solutions.
• Crop Management and Yield Prediction: Advanced analytics for crop management and yield prediction is a key growth area. By utilizing data from satellite imagery, weather forecasts, and historical crop performance, these tools provide accurate predictions of crop yields and health. This enables farmers to make informed decisions regarding planting, harvesting, and resource allocation. The ability to anticipate crop performance helps optimize agricultural planning and mitigate risks associated with yield variability, supporting overall farm productivity and profitability.
• Soil Health Monitoring: Soil health monitoring is gaining traction as a crucial application of agriculture analytics. Technologies that analyze soil properties, such as nutrient levels, moisture content, and pH, provide valuable insights for soil management. This application helps farmers maintain optimal soil conditions, improve crop growth, and sustain long-term soil fertility. As awareness of soil health's impact on agricultural sustainability grows, investment in soil monitoring solutions increases, presenting significant growth opportunities in this segment.
• Livestock Monitoring: Livestock monitoring is an emerging application of agriculture analytics that focuses on improving animal health and productivity. Solutions that use wearable sensors, GPS tracking, and data analytics provide real-time insights into animal behavior, health status, and productivity. This application enables farmers to detect issues early, optimize feeding practices, and enhance overall herd management. The growing emphasis on animal welfare and efficient livestock management drives the demand for advanced livestock monitoring technologies.
• Supply Chain Optimization: Supply chain optimization is a strategic growth opportunity within agriculture analytics, driven by the need for greater transparency and efficiency in food distribution. Data analytics platforms that track and analyze the movement of agricultural products from farm to table help streamline operations, reduce waste, and improve traceability. By enhancing supply chain management, these solutions support better decision-making and contribute to a more efficient and sustainable agricultural supply chain.

The strategic growth opportunities in agriculture analytics shape the future of the agricultural sector by enhancing precision, efficiency, and sustainability. Applications in precision agriculture, crop management, soil health monitoring, livestock monitoring, and supply chain optimization drive innovation and growth. As these opportunities continue to develop, they are expected to significantly impact agricultural practices, contributing to increased productivity and a more sustainable approach to farming.

Agriculture Analytic Market Driver and Challenges

The agriculture analytics market is influenced by various drivers and challenges that shape its development. Technological advancements, economic factors, and regulatory considerations play crucial roles in driving market growth and addressing obstacles. Understanding these drivers and challenges is essential for stakeholders to navigate the market effectively and leverage opportunities for growth while mitigating risks.

The factors responsible for driving the agriculture analytics market include:

• Technological Advancements: Rapid technological advancements in sensors, data analytics, and AI drive growth in agriculture analytics. Innovations such as advanced sensors for soil and crop monitoring, machine learning algorithms for predictive analytics, and drones for aerial imaging enhance data collection and analysis. These technologies enable precision farming, improve resource management, and optimize crop yields. As technology continues to evolve, it provides new capabilities and efficiencies that propel the agriculture analytics market forward.
• Increasing Demand for Food Security: The growing global population and increasing demand for food are significant drivers of the agriculture analytics market. To meet these demands, there is a need for enhanced agricultural productivity and efficiency. Agriculture analytics provides tools and insights that help optimize crop yields, manage resources effectively, and ensure food security. As the pressure to produce more food intensifies, the adoption of data-driven solutions becomes essential to achieving sustainable agricultural practices.
• Focus on Sustainability: Sustainability is a major driver of agriculture analytics, as there is a strong emphasis on reducing environmental impact and promoting eco-friendly farming practices. Analytics solutions that support precision agriculture, soil health monitoring, and resource optimization align with sustainability goals by minimizing waste and conserving resources. The increasing awareness of environmental issues and regulatory pressure to adopt sustainable practices drive the demand for analytics solutions that contribute to a greener agricultural sector.
• Government Support and Investment: Government initiatives and investments in agricultural technology and data analytics foster market growth. Many governments implement policies and provide funding to support the adoption of advanced technologies in agriculture. These investments help accelerate the development and deployment of agriculture analytics solutions, making them more accessible to farmers and enhancing the overall growth of the market.
• Growing Adoption of IoT and Big Data: The adoption of Internet of Things (IoT) devices and big data analytics drives innovation in agriculture. IoT sensors provide real-time data on various aspects of farming operations, while big data analytics platforms offer insights for decision-making. The integration of these technologies enables farmers to monitor and manage their operations more effectively, leading to increased efficiency and productivity. The widespread adoption of IoT and big data contributes significantly to the growth of agriculture analytics.

Challenges in the agriculture analytics market are:

• High Implementation Costs: One of the key challenges in the agriculture analytics market is the high cost of implementing advanced technologies. The initial investment required for sensors, data analytics platforms, and other technologies can be prohibitive for small and medium-sized farms. High implementation costs may limit the adoption of agriculture analytics solutions and create barriers to entry for some stakeholders, affecting overall market growth.
• Data Privacy and Security Concerns: Data privacy and security concerns are significant challenges in the agriculture analytics market. As agriculture analytics involves the collection and analysis of sensitive data, there is a risk of data breaches and misuse. Ensuring robust data protection measures and addressing privacy concerns are crucial for maintaining trust and encouraging the adoption of analytics solutions. The need for stringent security protocols presents an ongoing challenge for the market.
• Limited Access to Technology in Developing Regions: Limited access to advanced technology and infrastructure in developing regions poses a challenge for the agriculture analytics market. In regions with inadequate technological infrastructure, the adoption of data-driven solutions may be slow. Bridging the technology gap and providing affordable and accessible solutions are essential to expanding the reach of agriculture analytics and ensuring its benefits are realized globally.

The agriculture analytics market is significantly influenced by technological advancements, food security demands, sustainability goals, government support, and the adoption of IoT and big data. However, challenges such as high implementation costs, data privacy concerns, and limited technology access in developing regions also impact the market. Addressing these drivers and challenges is crucial for the continued growth and evolution of agriculture analytics, ensuring that the sector can meet its goals of increased productivity and sustainability.

List of Agriculture Analytic Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. through these strategies agriculture analytic companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the agriculture analytic companies profiled in this report include-

• Deere & Company
• IBM
• Bayer
• SAP
• Trimble
• Accenture
• ABACO
• DeLaval
• Oracle
• DTN

Agriculture Analytic by Segment

The study includes a forecast for the global agriculture analytic by agriculture type, farm size, technology, end use, and region.

Agriculture Analytic Market by Agriculture Type [Analysis by Value from 2019 to 2031]:

• Precision Farming
• Livestock Farming
• Aquaculture Farming
• Vertical Farming
• Others

Agriculture Analytic Market by Farm Size [Analysis by Value from 2019 to 2031]:

• Large Farms
• Small & Medium-Sized Farms

Agriculture Analytic Market by Technology [Analysis by Value from 2019 to 2031]:

• Remote Sensing & Satellite Imagery
• Geographic Information System
• Robotics & Automation
• Big Data & Cloud Computing
• Visualization & Reporting
• Blockchain Technology
• Others

Agriculture Analytic Market by End Use [Analysis by Value from 2019 to 2031]:

• Farmers
• Agronomists
• Agribusinesses
• Agricultural Researchers
• Government Agencies
• Others

Agriculture Analytic Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Agriculture Analytic Market

The agriculture analytics market is rapidly evolving, driven by technological advancements and a growing need for data-driven decision-making in farming. As global food demands rise and sustainability becomes paramount, countries are investing heavily in agricultural technology to optimize crop yields, improve resource management, and enhance overall productivity. Recent developments in the agriculture analytics market reflect this shift, with notable progress across the United States, China, Germany, India, and Japan. These advancements are shaping how data is used to address agricultural challenges and opportunities, transforming traditional farming practices into more efficient, technology-driven processes.

• United States: In the U.S., agriculture analytics is increasingly centered on precision farming and AI-driven insights. Major developments include the integration of satellite imagery and machine learning to optimize crop management. Companies like Trimble and John Deere lead innovations in precision agriculture, offering advanced tools for real-time data collection and analysis. These technologies help farmers increase yield, reduce waste, and manage resources more efficiently. The U.S. also sees significant investment in developing autonomous farming equipment, which aims to further enhance productivity and operational efficiency.
• China: China focuses on leveraging big data and IoT (Internet of Things) to revolutionize its agricultural sector. The government and private enterprises invest in smart farming solutions that include drone technology for crop monitoring and automated systems for precision irrigation. Companies such as DJI and Alibaba are at the forefront, using data analytics to improve crop forecasting and resource management. This push aligns with China's broader strategy to ensure food security and increase agricultural efficiency amid a growing population.
• Germany: Germany's advancements in agriculture analytics are driven by a strong emphasis on sustainability and precision farming. Innovations include the development of advanced sensor technologies and data analytics platforms that monitor soil health and optimize nutrient use. Companies like BASF and Bayer lead these initiatives, focusing on integrating digital solutions into traditional farming practices. Germany's approach highlights the importance of environmentally friendly farming techniques and the efficient use of resources to support sustainable agricultural practices.
• India: In India, agriculture analytics is rapidly evolving with a focus on improving smallholder farmer productivity. Developments include mobile applications and AI-powered platforms that provide real-time weather forecasts, pest detection, and crop recommendations. Startups such as AgroStar and CropIn are making significant strides in offering affordable, accessible analytics tools tailored to the needs of Indian farmers. These innovations aim to address challenges such as low productivity and resource constraints, helping farmers make informed decisions and improve their yields.
• Japan: Japan embraces advanced robotics and data analytics to address labor shortages and enhance agricultural efficiency. Developments include the use of automated systems for planting, harvesting, and monitoring crops. Companies like Kubota and Yamaha develop robotics solutions that integrate with data analytics to optimize farming operations. Additionally, Japan's focus on precision agriculture and smart farming technologies reflects its commitment to maximizing productivity while addressing challenges such as an aging farming population and limited arable land.

Features of the Global Agriculture Analytic Market

Market Size Estimates: Agriculture analytic market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Agriculture analytic market size by agriculture type, farm size, technology, end use, and region in terms of value ($B).

Regional Analysis: Agriculture analytic market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different agriculture type, farm size, technology, end use, and regions for the agriculture analytic market.

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the agriculture analytic market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the agriculture analytic market by agriculture type (precision farming, livestock farming, aquaculture farming, vertical farming, and others), farm size (large farms and small & medium-sized farms), technology (remote sensing & satellite imagery, geographic information system, robotics & automation, big data & cloud computing, visualization & reporting, blockchain technology, and others), end use (farmers, agronomists, agribusinesses, agricultural researchers, government agencies, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Agriculture Analytic Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Agriculture Analytic Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Agriculture Analytic Market by Agriculture Type
  • 3.3.1: Precision Farming
  • 3.3.2: Livestock Farming
  • 3.3.3: Aquaculture Farming
  • 3.3.4: Vertical Farming
  • 3.3.5: Others
• 3.4: Global Agriculture Analytic Market by Farm Size
  • 3.4.1: Large Farms
  • 3.4.2: Small & Medium-Sized Farms
• 3.5: Global Agriculture Analytic Market by Technology
  • 3.5.1: Remote Sensing & Satellite Imagery
  • 3.5.2: Geographic Information System
  • 3.5.3: Robotics & Automation
  • 3.5.4: Big Data & Cloud Computing
  • 3.5.5: Visualization & Reporting
  • 3.5.6: Blockchain Technology
  • 3.5.7: Others
• 3.6: Global Agriculture Analytic Market by End Use
  • 3.6.1: Farmers
  • 3.6.2: Agronomists
  • 3.6.3: Agribusinesses
  • 3.6.4: Agricultural Researchers
  • 3.6.5: Government Agencies
  • 3.6.6: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Agriculture Analytic Market by Region
• 4.2: North American Agriculture Analytic Market
  • 4.2.1: North American Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.2.2: North American Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.3: European Agriculture Analytic Market
  • 4.3.1: European Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.3.2: European Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.4: APAC Agriculture Analytic Market
  • 4.4.1: APAC Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.4.2: APAC Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.5: ROW Agriculture Analytic Market
  • 4.5.1: ROW Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.5.2: ROW Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Agriculture Analytic Market by Agriculture Type
  • 6.1.2: Growth Opportunities for the Global Agriculture Analytic Market by Farm Size
  • 6.1.3: Growth Opportunities for the Global Agriculture Analytic Market by Technology
  • 6.1.4: Growth Opportunities for the Global Agriculture Analytic Market by End Use
  • 6.1.5: Growth Opportunities for the Global Agriculture Analytic Market by Region
• 6.2: Emerging Trends in the Global Agriculture Analytic Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Agriculture Analytic Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Agriculture Analytic Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: Deere & Company
• 7.2: IBM
• 7.3: Bayer
• 7.4: SAP
• 7.5: Trimble
• 7.6: Accenture
• 7.7: ABACO
• 7.8: DeLaval
• 7.9: Oracle
• 7.10: DTN