Click on any of the images below to explore an interactive data visualization:This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data Visualizations For complete information, please visit https://data.gov.

Imagine you are a scientist studying how plants grow. Some plants grow better in certain conditions, like when they get the right amount of water, sunlight, and nutrients. Scientists collect information about plants to understand what helps them grow best. This collection of information is called a dataset.

The "Advanced Soybean Agricultural Dataset" is a special collection of information about soybean plants. It was created in 2025 by smart researchers at the College of Agriculture, University of Tikrit. These researchers wanted to help farmers grow better crops and improve food production.

Who Made This Dataset? Three important professors helped put this dataset together:

Assistant Lecturer Basim Fahad Abdullah Assistant Professor Dr. Dawood Salman Madad Assistant Professor Wisam Dawood Abdullah They studied thousands of soybean plants and wrote down important details about each one.

What’s Inside the Dataset? This dataset has 55,450 rows and 13 columns. That means it has a LOT of information! Each row represents one soybean plant, and each column tells us something important about it, like:

How tall the plant is How many pods (seed holders) it has How much it weighs How much chlorophyll it has (chlorophyll helps plants make food) How much protein is in the seeds How much water is inside the leaves How many seeds the plant produces Special Experimental Conditions The scientists also tested different conditions to see how they affect soybean growth. They recorded this information in a special column called "Parameters." The letters in this column mean different things:

G: The type (or "genotype") of the soybean plant (there are 6 types). C: If the plant was given salicylic acid (a natural plant booster). There were three levels: 250 mg 450 mg No salicylic acid (control group). S: How much water stress the plant experienced. There were two levels: Very little water (5% of field capacity) A lot more water (70% of field capacity) Why Is This Dataset Important? This dataset is super useful for scientists, farmers, and researchers. They can use this information to:

Predict how well soybean plants will grow in different conditions. Find the best ways to grow healthy and productive soybeans. Use computers and AI to help farmers make better decisions. This dataset is like a big book of plant secrets that can help improve farming and food production in the future!

Published by: Central Statistics Office

Licensed under: Creative Commons Attribution 4.0

Category: Agriculture

Ireland has an estimated 6.9million hectares of land, of which about 64%,or 4.44 million hectares, is suitable for agriculture. An estimated 770,020 hectares, or 11% of total land, is used for forestry (Department of Agriculture, Food and the Marine, 2020).

NASS Data Visualization provides a dynamic web query interface supporting searches by Commodity (e.g. Cotton, Corn, Farms & Land, Grapefruit, Hogs, Oranges, Soybeans, Wheat), Statistic type (automatically refreshed based upon choice of Commodity - e.g. Inventory, Head, Acres Planted, Acres Harvested, Production, Yield) to generate chart, table, and map visualizations by year (2001-2016), as well as a link to download the resulting data in CSV format compatible for updating databases and spreadsheets. Resources in this dataset:Resource Title: NASS Data Visualization web site. File Name: Web Page, url: https://nass.usda.gov/Data_Visualization/index.php Query interface with visualization of results as charts, tables, and maps.

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Precision Farming Market Size 2024-2028

The precision farming market size is forecast to increase by USD 2.82 billion, at a CAGR of 7% between 2023 and 2028.

Major Market Trends & Insights

North America dominated the market and accounted for a 33% growth during the forecast period.
By the Technology - Guidance technology segment was valued at USD 3.5 billion in 2022

Market Size & Forecast

Market Opportunities: USD 66.27 million
Market Future Opportunities: USD 2824.40 million
CAGR : 7%
North America: Largest market in 2022

Market Summary

Precision farming, a data-driven approach to agriculture, is experiencing significant advancements, with technological innovations and increasing adoption rates shaping the market's dynamics. According to recent studies, the market is projected to expand at a substantial pace, with a growing number of farmers recognizing the benefits of this technology. For instance, precision farming can enhance crop yields by up to 20%, while reducing water and fertilizer usage by up to 30% compared to traditional farming methods. The market's evolution is driven by advancements in satellite imagery, drones, sensors, and data analytics. These technologies enable farmers to monitor crop health, optimize irrigation, and manage fertilizer applications more effectively.
Moreover, the integration of artificial intelligence and machine learning algorithms further enhances precision farming's potential by providing real-time insights and predictive analytics. Despite these advancements, there remains a gap in precision farming adoption, particularly in economically developing and under-developed countries. However, as awareness about the technology's benefits grows, it is expected that the market will continue to expand, transforming agriculture and contributing to more sustainable farming practices worldwide.

What will be the Size of the Precision Farming Market during the forecast period?

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The market encompasses a range of advanced technologies and solutions that enhance agricultural productivity and efficiency. Two key components of this market are sensor data integration and cloud-based solutions. Prescription mapping tools, farm record-keeping systems, decision support systems, automation workflows, GIS data management, risk management strategies, equipment maintenance scheduling, and other precision farming technologies also contribute significantly to the market.
These solutions facilitate crop stress mitigation, soil nutrient management, nutrient deficiency detection, agricultural big data analysis, disease management strategies, water resource management, data visualization dashboards, integrated farm management, labor efficiency gains, smart farming technologies, connectivity solutions, crop growth modeling, farm operation scheduling, supply chain optimization, farm profitability metrics, yield prediction models, weed control technologies, and pest management strategies. The integration of these technologies leads to improved farm productivity, reduced costs, and enhanced sustainability.

How is this Precision Farming Industry segmented?

The precision farming industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Technology

  Guidance technology
  Remote sensing technology
  Variable-rate technology


Geography

  North America

    US
    Canada


  Europe

    Germany
    The Netherlands


  APAC

    China


  Rest of World (ROW)

By Technology Insights

The guidance technology segment is estimated to witness significant growth during the forecast period.

Precision farming, a modern agricultural approach, has witnessed significant advancements over the past decade. This technology-driven farming method focuses on optimizing resource utilization and enhancing crop productivity through the integration of drone technology applications, variable rate seeding, and variable rate technology. These innovations enable farmers to apply inputs more accurately, reducing waste and increasing efficiency. Nitrogen use efficiency is another crucial aspect of precision farming, with agricultural robotics and predictive modeling tools playing essential roles. Real-time data processing from remote sensing imagery and resource optimization strategies help farmers make informed decisions, leading to improved yield monitoring systems and harvest automation.

Moreover, precision farming encompasses environmental monitoring and sustainable farming practices. Sensor network deployment, soil health indicators, and crop health monitoring contribute to optimizing water use efficiency and minimizing in

This dataset simulates real-world smart farming operations powered by IoT sensors and satellite data. It captures environmental and operational variables that affect crop yield across 500 farms located in regions like India, the USA, and Africa.

Designed to reflect modern agritech systems, the data is ideal for: - Predictive modeling using ML/AI - Time-series analysis - Sensor-based optimization - Environmental data visualizations - Crop health analytics

🧠 Ideal For

• Supervised ML models (regression, classification)
• Yield prediction and optimization
• Agricultural decision support systems
• Smart irrigation strategy analysis
• Data visualization of regional farm efficiency

📌 Columns Description

📫 Let's Collaborate!

If you build a notebook, model, or dashboard using this dataset — feel free to tag me or leave a comment. Happy growing! 🌱🚜

Agricultural Districts as shown on the Sullivan County Tax Maps which are used for taxation and assessment. The Agricultural Districts feature layer is available for download by the public. Data can then be used to perform analysis, as a reference layer in maps created by the user, and/or for data visualization. Data is also used by GIS staff to create maps and apps for public use. Data was mapped using the NAD 1983 State Plane New York East FIPS 3101 Feet projection.

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Autonomous Crop Management Market Size 2024-2028

The autonomous crop management market size is forecast to increase by USD 5.76 billion at a CAGR of 10.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing focus on productivity and efficiency in the agriculture sector. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into autonomous crop management systems is driving this trend, enabling farmers to optimize crop yields and reduce operational costs. However, the high initial investment required for implementing these advanced technologies poses a significant challenge for many farmers and agricultural businesses. Despite this hurdle, the market's potential for innovation and improved agricultural outcomes is substantial. Companies seeking to capitalize on this opportunity should focus on developing cost-effective solutions that cater to the unique needs of various farming sectors and geographies.
Additionally, collaborations and partnerships with technology providers, agricultural institutions, and government organizations can help facilitate the adoption of autonomous crop management systems and mitigate the initial investment barrier. Overall, the market represents an exciting and dynamic landscape for businesses and investors alike, offering significant opportunities for innovation and growth in the agriculture sector.

What will be the Size of the Autonomous Crop Management Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and the growing demand for sustainable agriculture. Farmers are increasingly adopting solutions that leverage artificial intelligence, machine learning, and computer vision to optimize crop yield, improve harvest efficiency, and enhance farm management. Precision spraying and fertilizer management systems enable farmers to apply inputs more effectively, reducing waste and increasing profitability. autonomous vehicles and automated irrigation systems streamline farm operations, while soil health monitoring and variable rate application help improve crop production and reduce environmental impact. Farm management software and digital farming solutions offer real-time data integration, data visualization, and data-driven decision making, allowing farmers to optimize their operations and respond to changing conditions.
Drones and satellite imagery provide valuable insights into crop health and growth patterns, enabling farmers to make informed decisions and improve overall farm efficiency. The market for agricultural innovation is diverse, with a range of entities focusing on yield optimization, water conservation, and labor reduction. Smart sensors and GPS guidance systems enable farmers to monitor and manage their fields more effectively, while weather forecasting and disease management solutions help mitigate risks and protect crops. As the market for autonomous crop management continues to unfold, new applications and integrations are emerging. data security and data integration are becoming increasingly important, as farmers seek to protect their valuable agricultural data and leverage it to improve their operations.
The integration of carbon sequestration and sustainable agriculture solutions is also gaining momentum, as farmers seek to reduce their environmental footprint and enhance the long-term sustainability of their operations.

How is this Autonomous Crop Management Industry segmented?

The autonomous crop management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Hardware
  Software
  Services


Deployment

  On-premises
  Cloud-based


Technology

  IoT-Based Systems
  AI and Machine Learning
  Robotics


Application

  Precision Irrigation
  Weed Control
  Harvesting


Crop Type

  Cereals
  Fruits and Vegetables
  Oilseeds


Farm Size

  Large Farms
  Small and Medium Farms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

Autonomous crop management is revolutionizing agriculture through advanced technologies such as yield forecasting, carbon sequestration, and precision farming solutions. Agtech startups leverage satellite imagery and agricultural data to develop crop modeling and Farm Equipment automation, enhancing crop production and optimizing farm profitability. Farmers utilize mac

Agricultural Data Management Market Outlook

According to our latest research, the global agricultural data management market size reached USD 3.21 billion in 2024, reflecting a strong surge in adoption across diverse agricultural sectors. The market is projected to grow at a CAGR of 13.6% from 2025 to 2033, reaching a forecasted value of USD 9.06 billion by 2033. This robust growth is primarily driven by the increasing integration of advanced digital technologies in agriculture, rising demand for food security, and the need to optimize resource use for sustainability.

The growth trajectory of the agricultural data management market is underpinned by the rapid digitization of farming practices worldwide. Farmers and agribusinesses are increasingly leveraging data-driven solutions to enhance productivity, monitor crop health, and manage resources more efficiently. The proliferation of Internet of Things (IoT) devices, sensors, and drones has enabled real-time data collection, which, when integrated with sophisticated software platforms, allows for actionable insights and informed decision-making. This digital transformation is further catalyzed by supportive government policies promoting smart agriculture, as well as the growing need to address the challenges posed by climate change and limited arable land.

Another significant growth factor in the agricultural data management market is the escalating demand for precision farming solutions. Precision agriculture relies heavily on accurate and timely data to optimize planting, irrigation, fertilization, and pest management. The adoption of big data analytics, artificial intelligence, and machine learning in agriculture has empowered stakeholders to analyze large volumes of data from various sources—such as satellite imagery, weather stations, and field sensors—to maximize yields and minimize losses. This technological evolution has not only improved farm profitability but also contributed to environmental sustainability by reducing waste and resource overuse.

Moreover, the increasing emphasis on supply chain transparency and traceability is fueling the expansion of the agricultural data management market. Consumers, regulators, and supply chain partners are demanding greater visibility into the origins and handling of agricultural products. Advanced data management systems enable seamless tracking of produce from farm to fork, ensuring compliance with food safety standards and boosting consumer confidence. As global supply chains become more complex, the role of data management in ensuring quality, reducing spoilage, and optimizing logistics has become indispensable, driving further investments in this sector.

Regionally, North America currently dominates the agricultural data management market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The United States and Canada are at the forefront of adopting digital agriculture solutions, driven by large-scale commercial farming and strong technological infrastructure. Europe is witnessing significant growth due to stringent regulatory frameworks and sustainability initiatives. Meanwhile, the Asia Pacific region is poised for the fastest growth, propelled by increasing government investments in agri-tech, a rapidly expanding population, and a burgeoning need to enhance agricultural productivity to ensure food security.

Component Analysis

The component segment of the agricultural data management market is broadly categorized into software, hardware, and services. Software solutions form the backbone of data management by providing platforms for data collection, analytics, visualization, and reporting. These platforms are increasingly being designed with user-friendly interfaces and advanced analytics capabilities, enabling farmers to make data-driven decisions with ease. The integration of artificial intelligence and machine learning algorithms within these software tools further enhances predictive analytics, helping stakeholders ant

GPFARM (Great Plains Framework for Agricultural Resource Management) is a simulation model computer application. It incorporates state of the art knowledge in agronomy, animal science, economics, weed science and risk management into a user-friendly, decision support tool. Producers, agricultural consultants, action agencies and scientists can utilize GPFARM to test alternative management strategies that may in turn lead to sustainable agriculture, a reduction in pollution, or maximum economic return. GPFARM Express contains default projects to allow users to quickly set up their operations. GPFARM Decision Support System (DSS) Objective: Develop a resource management decision support system (DSS) that is capable of simulating and analyzing 10-50 year farm/ranch production plans with respect to water, nutrient, and pest management along with their associated economic and environmental risks. GPFARM DSS Benefits: GPFARM integrates state of the art agricultural science knowledge with associated economic and environmental analysis into a whole-enterprise evaluation. Results from the DSS provide agricultural consultants, producers, and action agencies with information for making management decisions that promote sustainable agriculture. GPFARM provides feedback concerning the most effective technology and assists in determining areas requiring further research and development. This is an evolutionary process that ties research and technology transfer closely together. GPFARM serves to bring scientists from different disciplines together with producers and consultants to solve complex problems in agriculture. Products within GPFARM: A user-friendly, farm/ranch simulation model that produces output for various agricultural production systems and management options with respect to economics, environmental impact and sustainability. A detailed whole farm/ranch economic analysis package (PAL Budgeting Program). A web based, encyclopedic agricultural information system. A stand-alone weed management model (WISDEM). Tools to analyze weed pressure effects and N fertilizer requirements. Analysis tools for results including output data visualization, indices and the Multiple Criterion Decision Making model. Spatial data visualization tools. Resources in this dataset:Resource Title: GPFARM. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=234&modecode=30-12-30-25 download page

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Agricultural Camera and Monitoring Systems Market Size 2024-2028

The agricultural camera and monitoring systems market size is forecast to increase by USD 7.39 billion at a CAGR of 8.71% between 2023 and 2028.

The market is witnessing significant growth due to the increasing adoption of drone technology and IoT-based smart agriculture monitoring systems. These advanced technologies enable farmers to remotely monitor crop health, soil moisture levels, and livestock welfare, leading to improved productivity and reduced operational costs. However, the high initial cost associated with these systems remains a significant challenge for smaller farming operations and developing economies.
Despite this obstacle, companies can capitalize on the market's potential by offering flexible financing options, partnerships with agricultural cooperatives, and continuous innovation to reduce costs while maintaining performance. As the agricultural sector continues to digitize, agricultural camera and monitoring systems will play a crucial role in optimizing farm operations and ensuring sustainable agricultural practices.

What will be the Size of the Agricultural Camera and Monitoring Systems Market during the forecast period?

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The market continues to evolve, integrating advanced technologies to enhance farm efficiency and productivity. These systems enable water conservation through automated irrigation and precision application of resources. Crop health assessment is achieved through remote sensing, drones, and image analysis, allowing farmers to detect diseases and pests early. Wireless communication and sensor networks facilitate real-time data collection and analysis, while data visualization tools provide insights for yield optimization and operational efficiency. Soil analysis and environmental monitoring ensure sustainable agriculture, while livestock health management and asset tracking maintain animal welfare and secure investments. Cloud computing and web-based platforms enable Data Integration, facilitating farm management and resource optimization.
The ongoing development of machine learning algorithms and artificial intelligence further enhance the capabilities of these systems, providing farmers with predictive analytics and precision agriculture solutions. The integration of autonomous vehicles, livestock behavior analysis, and weather forecasting adds to the market's dynamism, addressing the diverse needs of modern agriculture.

How is this Agricultural Camera and Monitoring Systems Industry segmented?

The agricultural camera and monitoring systems industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Agriculture monitoring
  Agriculture security


Component

  Hardware
  Software and services


Product Type

  Fixed Cameras
  Drone-Based Cameras
  Portable Cameras


Technology

  Infrared
  Hyperspectral
  Thermal Imaging


End-User

  Large-Scale Farms
  Small and Medium Farms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The agriculture monitoring segment is estimated to witness significant growth during the forecast period.

Agricultural monitoring systems have gained significant traction in the farming industry, leveraging advanced technologies such as machine learning, artificial intelligence, and remote sensing to enhance farm efficiency, optimize resources, and improve crop production. These systems facilitate disease identification, pest detection, and weather forecasting, enabling farmers to take proactive measures against potential threats. Autonomous vehicles and automated irrigation systems ensure water conservation and labor optimization, while livestock health management and animal behavior analysis contribute to animal welfare and food security. Sensor networks, wireless communication, and data visualization tools provide real-time insights into crop health assessment and soil analysis. Precision agriculture practices, including variable rate application and precision fertilization, rely on these monitoring systems to optimize yield and reduce operational costs.

The integration of cloud computing, analytics dashboards, and mobile applications streamlines data management and facilitates collaborative decision-making. Overall, agricultural monitoring systems foster sustainable agriculture, resource optimization, and operational efficiency, making them an essential investment for modern farming operations.

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The

Geo-Spatial Agriculture Analytics Market Outlook

According to our latest research, the Geo-Spatial Agriculture Analytics market size reached USD 1.92 billion in 2024, reflecting robust expansion across the sector. The market is set to grow at a CAGR of 13.7% over the forecast period, with projections indicating a value of USD 5.75 billion by 2033. This impressive growth is propelled by the increasing adoption of precision agriculture technologies, data-driven decision-making in farming, and the integration of advanced geo-spatial analytics for optimizing agricultural output and sustainability.

The primary growth driver for the Geo-Spatial Agriculture Analytics market is the growing need for precision farming solutions aimed at maximizing crop yields while minimizing resource utilization. As global food demand rises and arable land remains finite, farmers and agribusinesses are turning to geo-spatial analytics to gain actionable insights into soil health, crop status, and environmental conditions. The integration of satellite imagery, remote sensing, and real-time data analytics enables stakeholders to make informed decisions, reduce input costs, and enhance productivity. Moreover, government initiatives supporting smart agriculture and digital transformation in farming are further accelerating market adoption.

Another significant factor fueling market expansion is the proliferation of advanced technologies such as drones, IoT devices, and cloud-based analytics platforms. These innovations facilitate the collection and analysis of vast amounts of spatial and temporal data, providing granular visibility into field conditions and crop performance. The synergy between geo-spatial analytics and emerging technologies not only streamlines farm operations but also empowers stakeholders with predictive capabilities for yield estimation, pest management, and irrigation planning. As a result, the market is witnessing increased investments from both public and private sectors, driving sustained growth.

The rising concerns over climate change and its impact on agricultural productivity have also intensified the adoption of geo-spatial analytics. Unpredictable weather patterns, soil degradation, and water scarcity are compelling farmers and agribusinesses to leverage advanced analytics for risk mitigation and sustainable resource management. By integrating climate models, soil data, and crop health monitoring, geo-spatial analytics solutions enable adaptive strategies that enhance resilience and ensure food security. The convergence of environmental sustainability goals and technological advancements is thus a key catalyst for the market's upward trajectory.

From a regional perspective, North America currently dominates the Geo-Spatial Agriculture Analytics market, owing to its early adoption of precision agriculture, well-established technology infrastructure, and supportive regulatory environment. However, Asia Pacific is poised for the fastest growth, driven by increasing investments in agricultural modernization, rising awareness among farmers, and government-led digital agriculture initiatives. Europe and Latin America are also witnessing steady adoption, supported by strong research and innovation ecosystems. The Middle East & Africa, while at a nascent stage, is expected to gain momentum as digital agriculture solutions become more accessible and affordable.

Component Analysis

The Geo-Spatial Agriculture Analytics market is segmented by component into software, hardware, and services. The software segment commands the largest share, owing to the critical role of analytics platforms, data visualization tools, and farm management systems in transforming raw data into actionable insights. These software solutions leverage machine learning, artificial intelligence, and geospatial algorithms to identify trends, predict outcomes, and optimize farm operations. With the increasing complexity of agricultural data and the need for real-time decision-making, software providers are focusing on enhancing user experience, interoperability, and scalability, which is driving robust demand in this segment.

Hardware components, including sensors, GPS devices, drones, and remote sensing equipment, form the backbone of data acquisition in geo-spatial agriculture analytics. The hardware segment is witnessing rapid innovation, with advancements in sensor accuracy, drone flight du

The Augmented Reality (AR) in Agriculture market is poised for substantial growth, projected to reach a market size of approximately $1,200 million by 2025 and expanding to an estimated $2,500 million by 2033, with a Compound Annual Growth Rate (CAGR) of around 9%. This dynamic expansion is fueled by the increasing need for precision farming techniques, labor efficiency, and data-driven decision-making across the agricultural value chain. AR's ability to overlay real-time digital information onto the physical world offers unparalleled benefits in areas such as pest and disease detection, enabling farmers to identify and address issues more quickly and accurately. Weather monitoring applications leverage AR to provide on-the-spot visualizations of forecasts and current conditions, facilitating proactive crop management. Furthermore, geological inspections are enhanced through AR, allowing for better soil analysis and land utilization planning. The integration of AR is not limited to traditional farming; it is also gaining traction in indoor agriculture settings, optimizing resource allocation and environmental control for controlled farming environments. Key drivers for this market include the escalating demand for increased crop yields and improved food quality to feed a growing global population, coupled with the scarcity of skilled agricultural labor. AR solutions address these challenges by empowering farmers with intuitive, visual tools that simplify complex tasks and reduce the reliance on extensive manual labor. Leading companies like Microsoft, Alphabet, and Trimble are investing heavily in AR technologies, developing innovative solutions that span from remote assistance for field technicians to AI-powered crop diagnostics. The market is further segmented by application, with outdoor agriculture currently dominating but indoor agriculture showing significant growth potential due to the rise of vertical farming and controlled environment agriculture. Emerging trends such as the use of AR for training and education in agriculture, and the integration with IoT devices for comprehensive data visualization, are expected to shape the market's trajectory in the coming years. While the initial investment in AR technology can be a restraint, the long-term benefits in terms of increased productivity, reduced waste, and improved resource management are making AR an increasingly attractive proposition for modern agricultural operations. Here's a unique report description on Augmented Reality (AR) in Agriculture, designed to be informative and engaging for potential readers.

This comprehensive report provides an in-depth analysis of the Augmented Reality (AR) in Agriculture market, charting its trajectory from 2019 to 2033. We delve into the transformative potential of AR technologies in revolutionizing agricultural practices, enhancing efficiency, and driving sustainable growth. Our analysis covers the historical landscape (2019-2024), the current market scenario (Base Year: 2025), and provides robust projections for the future (Forecast Period: 2025-2033). The report will highlight the significant market insights, driving forces, prevailing challenges, dominant regions and segments, key growth catalysts, and the leading players shaping this dynamic industry.

This dataset includes following:The shapefile of the study area location: Krishi Vigyan Kendra (KVK) Dhanauri, Roorkee, Uttarakhand, IndiaField imagesAnalysis codes and sample imagery for Phenocam, Sentinel-2 and Planetscope.

Farm Analytics Market Outlook

According to our latest research, the global farm analytics market size reached USD 1.65 billion in 2024 and is projected to expand at a compound annual growth rate (CAGR) of 13.4% through 2033, reaching a forecasted market size of USD 5.13 billion. The robust growth of the farm analytics market is primarily driven by increasing adoption of data-driven decision-making in agriculture, the need for optimized resource utilization, and the growing emphasis on sustainable farming practices.

The growth of the farm analytics market is underpinned by the rising global demand for food due to population growth and the consequent pressure on agricultural productivity. Farmers and agribusinesses are increasingly turning to advanced analytics solutions to enhance crop yields, reduce wastage, and manage resources more efficiently. The integration of Internet of Things (IoT) devices, remote sensing technologies, and big data analytics has revolutionized farm management by providing actionable insights into soil health, weather patterns, crop performance, and pest infestations. These innovations enable precise interventions, reducing the risks associated with unpredictable weather and fluctuating market conditions. Furthermore, the proliferation of affordable sensors and cloud-based platforms has democratized access to analytics, enabling even small and medium-sized farms to leverage these technologies for improved outcomes.

Another significant growth factor for the farm analytics market is the increasing focus on sustainability and regulatory compliance. Governments and international agencies are promoting sustainable agricultural practices to address environmental concerns such as soil degradation, water scarcity, and greenhouse gas emissions. Farm analytics solutions help farmers comply with these regulations by monitoring input usage, tracking carbon footprints, and ensuring environmentally friendly operations. The availability of real-time data and predictive analytics empowers farmers to make informed decisions that balance productivity with ecological stewardship. Additionally, the rise in precision agriculture, which relies heavily on analytics, is driving market growth as it enables targeted application of fertilizers, pesticides, and water, minimizing environmental impact while maximizing yields.

The digital transformation of agriculture, supported by significant investments from both public and private sectors, is further accelerating the adoption of farm analytics. Governments are launching initiatives to modernize agricultural infrastructure and provide digital literacy training to farmers, while agritech startups and established technology companies are introducing innovative analytics platforms tailored to the unique needs of the agriculture sector. The increasing penetration of smartphones and high-speed internet in rural areas is making it easier for farmers to access and utilize analytics tools. Furthermore, partnerships between technology providers, agricultural cooperatives, and research institutes are fostering the development of localized analytics solutions that address region-specific challenges, thereby expanding the market’s reach and impact.

From a regional perspective, North America currently dominates the farm analytics market, owing to its advanced agricultural practices, high adoption of precision farming technologies, and strong presence of leading analytics vendors. Europe follows closely, driven by stringent environmental regulations and a strong focus on sustainable agriculture. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by large-scale government initiatives, increasing investment in agritech, and a rapidly expanding population that is driving demand for higher agricultural productivity. Latin America and the Middle East & Africa are also showing promising growth, supported by efforts to modernize traditional farming methods and improve food security.

Component Analysis

The component segment of the farm analytics market is categorized into software, hardware, and services, each playing a pivotal role in transforming agricultural operations. Software solutions represent the backbone of farm analytics, enabling data collection, processing, visualization, and actionable insight generation. These platforms often integrate with farm management systems, IoT devices, and satellite im

This dataset is generated from USDA - United States Department of Agriculture consisting of coffee production, supply, and distribution - Commodity chosen is Coffee, Green - Consists of 94 countries. - Years taken into consideration: 1960-2023 - Unit Description for attribute values is: 1000, 60-kg bags

Visualized the dataset in Tableau

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Corn productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Corn - Operations with SalesCorn - Sales, Measured in US Dollars ($)Corn, Grain - Acres HarvestedCorn, Grain - Operations with Area Harvested - Area Harvested: (1.0 to 24.9 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (25.0 to 99.9 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (100 to 249 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (250 to 499 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (500 to 999 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (1,000 or More Acres)Corn, Grain - Operations with Area HarvestedCorn, Grain - Production, Measured in BushelsCorn, Grain, Irrigated - Acres HarvestedCorn, Grain, Irrigated - Operations with Area HarvestedCorn, Silage - Acres HarvestedCorn, Silage - Operations with Area Harvested - Area Harvested: (1.0 to 24.9 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (25.0 to 99.9 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (100 to 249 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (250 to 499 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (500 to 999 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (1,000 or More Acres)Corn, Silage - Operations with Area HarvestedCorn, Silage - Production, Measured in TonsCorn, Silage, Irrigated - Acres HarvestedCorn, Silage, Irrigated - Operations with Area HarvestedCorn, Traditional or Indian - Acres HarvestedCorn, Traditional or Indian - Operations with Area HarvestedCorn, Traditional or Indian - Production, Measured in lbsCorn, Traditional or Indian, Irrigated - Acres HarvestedCorn, Traditional or Indian, Irrigated - Operations with Area Harvested Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

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District-wise yearly area, yield and production. This file group has two files: 1. Area, production and yield and 2. High yielding varieties The area, production yield file includes data on 20 major crops that include cereals, pulses, oilseeds, cotton, sugarcane, total fruits and vegetables. Yield is calculated based on area and production.

The data are for the annual area and production under the crops. The percent area under each crop is calculated by dividing crop area by Gross Cropped Area (GCA variable generated using a defined methodology).

For more details see definition and standards and in the data documentation manual the section on ‘data clarification and anomalies’.For season wise crop area and production data refer to season wise area and production of crops under additional data); for breakup of fruits and vegetables data by type also see files on area and vegetables under additional data.

The second file is on High Yielding Varieties (HYV / hybrids) has data on area under HYVs for 5 major cereal crops. The data on HYVs has a number of gaps in recent years implying that the area is completely under HYVs and hence no longer reported / some states do not publish this data.

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This project includes a pdf capture of a webpage and the underlying data for the visualizations in both csv and Tableau formats.On July 28-30, 2017, the VA Office of Enterprise Integration and the University of California Agriculture and Natural Resources (UCANR) convened a 48-hour collaborative event at the Urban Hive in Sacramento, California, to encourage the development of innovative solutions to spark entrepreneurship and bring together the seemingly disparate worlds of software development, commercial farming, and Veterans.Data about Veteran farmers by county was also used at Tableau’s Student Data Hackathon on July 31, 2018, where Washington D.C. area college students, who are Veterans, learned about Tableau products using data from VA’s Open Data and the Bureau of Labor Statistics to build data analytics skills creating data visualizations.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Turkey productionProjection: Web Mercator Auxiliary SphereGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. ou should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Turkeys - InventoryTurkeys - Operations with InventoryTurkeys - Operations with SalesTurkeys - Sales, Measured in HeadTurkeys, Production Contract - Operations with ProductionTurkeys, Production Contract - Production, Measured in Head Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.