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精密農業

市場調查報告書

商品編碼

1319250

全球農業分析市場 - 2023-2030 年

Global Agriculture Analytics Market - 2023-2030

出版日期: 2023年07月31日 | 出版商:

DataM Intelligence | 英文 190 Pages | 商品交期: 最快1-2個工作天內

價格

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簡介目錄

市場概述

全球農業分析市場規模在2022 年達到12 億美元，預計到2030 年將達到28 億美元，2023-2030 年的年複合成長率為11.5%。

農業分析是指利用先進技術、數據分析和預測建模技術，在農業領域獲得洞察力並做出明智決策。精準農業需要利用技術和資訊，根據具體地點最佳化耕作方法。它利用感測器、無人機和GPS 系統等工具，積累有關土壤條件、作物生長和環境因素的記錄。

數據驅動型農業是指利用通過感測器、衛星圖像和氣象站等各種資源收集的農業記錄，為農業生產決策提供資訊的做法。

人工智慧（AI）和機器學習（ML）技術在農業分析中發揮著重要作用。人工智慧和ML 算法用於分析大量農業數據、識別模式並生成預測模型。這些模型有助於預測作物產量、疾病爆發、天氣模式以及最佳化資源分配，從而提高決策和營運效率。

市場動態

全球糧食生產需求的成長推動了農業分析市場的發展

全球人口持續大幅成長。據聯合國糧農組織（FAO）預測，穀物產量年成長率將放緩至0.7%（發展中國家為0.8%），到2050 年，穀物平均產量將從3.2 噸/公頃增至4.3 噸/公頃左右。根據同一資料來源，預計到2050 年，世界人口將達到97 億。人口激增給農業區帶來了壓力，需要提供額外的糧食來滿足不斷成長的需求。

快速的城市化和生活方式的轉變帶來了從傳統的生計農業向商業農業的轉變。這種轉變要求改進農業實踐，採用先進技術，包括農業分析技術，以提高糧食生產的生產力和績效。

物聯網（IoT）與感測器技術的融合有望促進農業分析市場的發展

物聯網和感測器技術在農業領域的結合，實現了對土壤濕度、溫度、濕度和作物健康等眾多參數的獨特追踪和控制。根據世界經濟論壇的一份報告，在農業中採用物聯網技術可使用水量減少10-15%，化學投入減少20-30%。這些技術提供的即時統計數據可通過農業分析進行分析，使農民能夠最佳化資源配置，減少浪費，並裝飾常見的農業性能。

物聯網和感測器技術可以從農田、牲畜和機械中持續收集數據。這些即時數據可通過農業分析工具進行分析，為決策提供有價值的見解。例如，感測器可以提供土壤濕度數據，使農民能夠精確安排灌溉時間。通過即時數據分析，可以主動應對不斷變化的條件，從而改進農場管理方法，提高生產率。

缺乏認知和技術專長阻礙了農業分析市場的發展

農業分析需要一定的數據分析、解釋和分析工具使用方面的專業技術知識。然而，許多農民和農業專業人員可能不具備這些方面的必要技能或知識。他們可能不熟悉統計分析、建模技術和數據可視化。這種知識差距會阻礙農業分析解決方案的實施和有效利用。

農民在獲取提供農業分析指導的培訓計劃或支持系統方面往往面臨挑戰。培訓資源、研討會或專家援助有限，會阻礙數據分析技術專業知識的發展和分析工具的實際應用。缺乏可利用的培訓和支持機制加劇了技術專長方面的差距。

COVID-19 影響分析

COVID-19 分析包括COVID 前情景、COVID 情景和COVID 後情景，以及定價動態（包括大流行期間和之後的定價變化，並與COVID 前情景進行比較）、供求光譜（由於交易限制、封鎖和後續問題而導致的供需變化）、政府計劃（政府機構為振興市場、部門或行業而採取的計劃）和製造商戰略計劃（此處將涵蓋製造商為緩解COVID 問題而採取的措施）。

Market Overview

Global Agriculture Analytics Market reached US$ 1.2 billion in 2022 and is expected to reach US$ 2.8 billion by 2030 growing with a CAGR of 11.5% during the forecast period 2023-2030.

Agriculture analytics refers to the use of advanced technologies, data analysis, and predictive modeling techniques to gain insights and make informed decisions in the field of agriculture. Precision agriculture entails the use of technology and information to optimize farming practices on a site-specific basis. It utilizes tools along with sensors, drones, and GPS systems to accumulate records about soil conditions, crop growth, and environmental factors.

Data-driven farming refers to the practice of using agricultural records, collected through various resources inclusive of sensors, satellite imagery, and weather stations, to inform decision-making in farming operations.

Artificial intelligence (AI) and machine studying (ML) techniques are playing a important role in agriculture analytics. AI and ML algorithms are used to analyze large volumes of agricultural data, identify patterns, and generate predictive models. These models assist in predicting crop yields, disorder outbreaks, weather patterns, and optimizing resource allocation for improved decision-making and operational efficiency.

Market Dynamics

Increasing Demand for Food Production Globally is Driving the Agriculture Analytics Market

The global population continues to increase at a substantial rate. According to the Food and Agriculture Organization (FAO), cereal yield growth would slowdown to 0.7 percent per annum (0.8 percent in developing countries), and average cereal yield would by 2050 reach around 4.3 ton/ha, up from 3.2 ton/ha. According to the same source, the world population is projected to reach 9.7 billion by 2050. This population boom puts pressure at the agriculture zone to supply extra food to fulfill the rising demand.

Rapid urbanization and converting life have brought about a shift from conventional subsistence farming to commercial agriculture. This shift necessitates improved agricultural practices and the adoption of advanced technology, together with agriculture analytics, to boom productivity and performance in food production.

Integration of Internet of Things (IoT) and Sensor Technologies is Expected to Foster the Agriculture Analytics Market

The mixing of IoT and sensor technology in agriculture enables unique tracking and control of numerous parameters such as soil moisture, temperature, humidity, and crop health. According to a report by the World Economic Forum, the adoption of IoT in agriculture can lead to a 10-15% reduction in water usage and a 20-30% reduction in chemical inputs. Those technologies provide real-time statistics that may be analyzed through agriculture analytics, allowing farmers to optimize resource allocation, reduce waste, and decorate common farming performance.

IoT and sensor technology enable continuous data collection from agricultural fields, livestock, and machinery. This real-time data can be analyzed using agriculture analytics tools to provide valuable insights for decision-making. For instance, sensors can provide data on soil moisture levels, allowing farmers to precisely schedule irrigation. Real-time data analysis enables proactive responses to changing conditions, leading to improved farm management practices and increased productivity.

Lack of Awareness and Technical Expertise is Holding Back the Agriculture Analytics Market

Agriculture analytics requires a certain level of technical expertise in data analysis, interpretation, and the use of analytics tools. However, many farmers and agricultural professionals may not possess the necessary skills or knowledge in these areas. They may lack familiarity with statistical analysis, modeling techniques, and data visualization. This knowledge gap can impede the implementation and effective utilization of agriculture analytics solutions.

Farmers often face challenges in accessing training programs or support systems that provide guidance on agriculture analytics. Limited availability of training resources, workshops, or expert assistance can hinder the development of technical expertise in data analysis and the practical application of analytics tools. The lack of accessible training and support mechanisms exacerbates the gap in technical expertise.

COVID-19 Impact Analysis

The COVID-19 Analysis includes Pre-COVID Scenario, COVID Scenario, and Post-COVID Scenario along with Pricing Dynamics (Including pricing change during and post-pandemic comparing it with pre-COVID scenarios), Demand-Supply Spectrum (Shift in demand and supply owing to trading restrictions, lockdown, and subsequent issues), Government Initiatives (Initiatives to revive market, sector or Industry by Government Bodies) and Manufacturers Strategic Initiatives (What manufacturers did to mitigate the COVID issues will be covered here).

Segment Analysis

The global agriculture analytics market is segmented based on source, packaging, distribution channel, and region.

By Deployment, the Cloud Segment is Estimated to have Significant Growth During the Forecast Period

Cloud-based solutions offer scalability and flexibility, allowing users to scale their storage and computing resources based on their needs. This scalability is particularly valuable in the agriculture industry, where data volumes can vary significantly throughout the agricultural cycle. According to a journal published by Frontiers, Automation and the use of artificial intelligence (AI), internet of things (IoT), drones, robots, and Big Data serve as a basis for a global "Digital Twin," which will contribute to the development of site-specific conservation and management practices that will increase incomes and global sustainability of agricultural systems.

Cloud-based agriculture analytics platforms can accommodate the storage and processing requirements of large and diverse agricultural datasets. The cloud enables easy access to data from anywhere, anytime, as long as there is an internet connection. This accessibility promotes collaboration and data sharing among stakeholders in the agriculture ecosystem, including farmers, researchers, consultants, and agribusinesses. Cloud-based platforms facilitate real-time data access, analytics, and collaborative decision-making, contributing to the overall adoption and dominance of the cloud segment.

Geographical Analysis

Asia Pacific is the Fastest Growing Market in the Agriculture Analytics Market

Asia Pacific is domestic to a large agricultural area and a vast population engaged in farming. The increasing demand for meals, coupled with the need to beautify agricultural productiveness, has led to the adoption of superior technology and analytics solutions. According to Asia Development Bank, with 76% of Asia's poor living in rural areas, raising agricultural productivity and income is key to fighting poverty. By leveraging agriculture analytics, farmers in the region can optimize resource utilization, implement precision farming practices, and improve overall productivity.

Precision agriculture techniques, which heavily rely upon data-driven insights and analytics, have received traction inside the Asia Pacific region. Farmers are increasingly more adopting technology inclusive of sensors, drones, and satellite imagery to monitor crops, analyze soil conditions, and optimize resource management. Agriculture analytics performs a important role in analyzing the collected data and supplying actionable insights for precision agriculture, contributing to the market growth inside the region.

Competitive Landscape

The major global players in the market include: Trimble Inc., Bayer AG, IBM Corporation, Deere & Company, Ageagle Aerial Systems Inc, Vistex, Inc., Agrivi, SAS Institute Inc., Conservis Corporation, and Iteris Inc.

Why Purchase the Report?

To visualize the global agriculture analytics market segmentation based on component, application, deployment, farm size, and region, as well as understand key commercial assets and players.
Identify commercial opportunities in the market by analyzing trends and co-development.
Excel data sheet with numerous data points of agriculture analytics market-level with all segments.
The PDF report consists of cogently put-together market analysis after exhaustive qualitative interviews and in-depth market study.
Product mapping is available as Excel consists of key products of all the major market players.

The global agriculture analytics market report would provide approximately 69 tables, 65 figures and 190 Pages.

Target Audience 2023

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. Market Definition and Overview

3. Executive Summary

3.1. Market Snippet, by Component
3.2. Market Snippet, by Application
3.3. Market Snippet, by Deployment
3.4. Market Snippet, by Farm Size
3.5. Market Snippet, by Region

4. Market Dynamics

4.1. Market Impacting Factors
- 4.1.1. Drivers
- 4.1.2. Restraints
- 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. Pricing Analysis
5.4. Regulatory Analysis

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID-19
- 6.1.2. Scenario During COVID-19
- 6.1.3. Scenario Post COVID-19
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Component

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 7.1.2. Market Attractiveness Index, By Component
7.2. Solution*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Services

8. By Application

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 8.1.2. Market Attractiveness Index, By Application
8.2. Farm Analytics*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Livestock Analytics
8.4. Aquaculture
8.5. Others

9. By Deployment

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 9.1.2. Market Attractiveness Index, By Deployment
9.2. Cloud*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. On-Premises

10. By Farm Size

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
- 10.1.2. Market Attractiveness Index, By Farm Size
10.2. Large Farms*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Small & Medium Farms

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America*
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.7.1. The U.S.
  - 11.2.7.2. Canada
  - 11.2.7.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.7.1. Germany
  - 11.3.7.2. The U.K.
  - 11.3.7.3. France
  - 11.3.7.4. Italy
  - 11.3.7.5. Spain
  - 11.3.7.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.7.1. Brazil
  - 11.4.7.2. Argentina
  - 11.4.7.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.7.1. China
  - 11.5.7.2. India
  - 11.5.7.3. Japan
  - 11.5.7.4. Australia
  - 11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. Trimble Inc.*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. Bayer AG
13.3. IBM Corporation
13.4. Deere & Company
13.5. Ageagle Aerial Systems Inc
13.6. Vistex, Inc.
13.7. Agrivi
13.8. SAS Institute Inc.
13.9. Conservis Corporation
13.10. Iteris Inc

LIST NOT EXHAUSTIVE