AI-Powered Yield Forecasting Market Forecasts to 2032 – Global Analysis By FilmType (Metallized Barrier Films, Transparent Barrier Films, Co-Extruded Multilayer Films, Laminated Films and Other Film Types), Material, Thickness, Packaging Format, Technolog
Description
According to Stratistics MRC, the Global AI-Powered Yield Forecasting Market is accounted for $1.6 billion in 2025 and is expected to reach $4.9 billion by 2032 growing at a CAGR of 17% during the forecast period. AI-powered yield forecasting refers to the use of advanced artificial intelligence techniques—such as machine learning, deep learning, and predictive analytics—to estimate future crop yields with high accuracy. It analyzes vast datasets including weather patterns, soil conditions, satellite imagery, historical yield records, and real-time farm inputs to identify patterns and predict productivity. By continuously learning from new data, AI systems deliver dynamic, location-specific, and timely forecasts. This helps farmers optimize resource allocation, plan harvesting, manage risks, and improve profitability. Overall, AI-powered yield forecasting enhances decision-making by transforming complex agricultural data into actionable insights for sustainable farming.
Market Dynamics:
Driver:
Growing demand for accurate crop predictions
Farmers and agribusinesses increasingly rely on predictive analytics to anticipate yields, optimize resource allocation, and reduce risks. AI models integrate satellite imagery, weather data, and soil conditions to deliver precise forecasts, improving decision-making. Accurate predictions help mitigate the impact of climate variability and ensure food supply stability. Governments and cooperatives are also adopting AI forecasting to strengthen food security planning. Rising global population and pressure on agricultural systems further amplify the need for reliable yield estimates.
Restraint:
High implementation and maintenance costs
Deploying AI-powered forecasting systems requires investment in sensors, data infrastructure, and advanced software platforms. Small and medium-sized farmers often struggle to afford these technologies, limiting adoption. Maintenance costs, including regular updates and technical support, add to the financial burden. Integration with existing farm management systems can also be complex and resource-intensive. These challenges slow penetration in cost-sensitive regions and among fragmented landholdings. Consequently, high costs remain a significant restraint to widespread adoption of AI-powered yield forecasting solutions.
Opportunity:
Rising need for optimized farm productivity
AI-powered forecasting enables farmers to plan planting schedules, irrigation, and harvesting with greater precision. This optimization reduces waste, enhances resource efficiency, and maximizes yields. As global food demand continues to rise, productivity improvements are critical to meeting supply requirements. AI solutions also support sustainable farming practices by minimizing environmental impact through data-driven decisions. Governments and agritech firms are increasingly promoting AI adoption to achieve food security and sustainability goals. As a result, the need for optimized productivity is expected to unlock substantial growth opportunities for the market.
Threat:
Dependence on quality and availability of data
Inaccurate or incomplete datasets can lead to unreliable predictions, undermining farmer confidence. Many regions lack robust data infrastructure, limiting the scope of AI applications. Seasonal variability and inconsistent weather records further challenge model accuracy. Data privacy concerns also restrict access to farm-level information, slowing adoption. Without high-quality inputs, AI systems cannot deliver the precision required for effective forecasting. Consequently, dependence on data availability remains a critical threat to market credibility and growth.
Covid-19 Impact:
The COVID-19 pandemic had a mixed impact on the AI-powered yield forecasting market. Supply chain disruptions delayed deployment of sensors and data infrastructure, slowing adoption in several regions. Farmers faced financial uncertainty, reducing investment in advanced technologies during the crisis. However, the pandemic highlighted the importance of resilience and efficiency in agriculture, driving renewed interest in predictive solutions. Remote monitoring and digital platforms gained traction as physical access to farms was restricted. Governments also emphasized food security, accelerating adoption of AI forecasting tools.
The polyethylene (PE) segment is expected to be the largest during the forecast period
The polyethylene (PE) segment is expected to account for the largest market share during the forecast period, driven by its widespread use in agricultural applications. PE films and coverings are integral to data collection systems, enabling controlled environments for accurate yield forecasting. Their durability, cost-effectiveness, and versatility make them the preferred material for protective and monitoring solutions. Farmers rely on PE-based infrastructure to support AI-driven sensors and imaging devices. The segment benefits from strong demand across both developed and emerging markets. Rising adoption of AI forecasting tools further reinforces the importance of PE materials in agricultural setups.
The transparent barrier films segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the transparent barrier films segment is predicted to witness the highest growth rate due to its role in enhancing data accuracy. These films allow optimal visibility for sensors and imaging devices, improving the precision of AI-powered forecasts. Their lightweight and flexible properties make them suitable for diverse agricultural applications. Rising demand for advanced monitoring solutions is accelerating adoption of transparent barrier films. Manufacturers are innovating with sustainable and high-performance materials to meet evolving needs. Integration with smart farming infrastructure further strengthens the segment’s growth trajectory.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share driven by advanced agricultural infrastructure. Farmers in the United States and Canada are leveraging predictive analytics to optimize yields and resource use. Strong government support and investment in agritech innovation reinforce regional leadership. The presence of leading AI firms and agricultural cooperatives accelerates commercialization of forecasting solutions. High awareness of sustainability and efficiency further strengthens demand. Retail and cooperative networks also facilitate widespread adoption of AI-powered tools.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR by rising food demand. Countries such as China, India, and Australia are increasingly adopting AI-powered forecasting to improve productivity. Expanding middle-class populations and government initiatives promoting smart farming support adoption. Farmers in the region are becoming more aware of the benefits of predictive analytics in managing risks. E-commerce and digital platforms are making AI solutions more accessible across diverse markets. Rising investment in agritech startups further accelerates regional growth.
Key players in the market
Some of the key players in AI-Powered Yield Forecasting Market include IBM, Microsoft, Google, Amazon Web Services, SAP SE, Oracle Corporation, Siemens AG, Deere & Company (John Deere), AG Leader Technology, Trimble Inc., Climate LLC, Granular (Corteva Agriscience), Prospera Technologies, Taranis and CropX Technologies.
Key Developments:
In May 2024, Microsoft announced major new AI and cloud capabilities within its Azure AI Services, including updates to Azure OpenAI Service. These enhancements empower developers and agri-tech companies to build more sophisticated predictive analytics tools on the Azure platform, directly improving the power and accessibility of AI-driven yield forecasting solutions for farmers.
In February 2023, IBM partnered with NASA to deploy its foundational AI model for geospatial data, aiming to vastly improve climate and agricultural analytics. This collaboration enhances the ability to predict crop yields by analyzing environmental factors like soil moisture and land use from satellite imagery with unprecedented accuracy, providing a powerful tool for the agricultural sector.
Film Types Covered:
• Metallized Barrier Films
• Transparent Barrier Films
• Co-Extruded Multilayer Films
• Laminated Films
• Other Film Types
Materials Covered:
• Polyethylene (PE)
• Polypropylene (PP)
• Polyethylene Terephthalate (PET)
• Ethylene Vinyl Alcohol (EVOH)
• Polyvinylidene Chloride (PVDC)
• Other Materials
Thicknesses Covered:
• Below 50 microns
• 50–100 microns
• 100–150 microns
• Above 150 microns
Packaging Formats Covered:
• Pouches
• Bags
• Lidding Films
• Wraps & Sheets
• Blisters
• Other Packaging Formats
Technologies Covered:
• Extrusion Coating
• Solvent-Based Coating
• Solvent-Free Coating
• Water-Based Coating
• Other Technologies
End Users Covered:
• Personal Care & Cosmetics
• Household Products
• Industrial Products
• Other End Users
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Market Dynamics:
Driver:
Growing demand for accurate crop predictions
Farmers and agribusinesses increasingly rely on predictive analytics to anticipate yields, optimize resource allocation, and reduce risks. AI models integrate satellite imagery, weather data, and soil conditions to deliver precise forecasts, improving decision-making. Accurate predictions help mitigate the impact of climate variability and ensure food supply stability. Governments and cooperatives are also adopting AI forecasting to strengthen food security planning. Rising global population and pressure on agricultural systems further amplify the need for reliable yield estimates.
Restraint:
High implementation and maintenance costs
Deploying AI-powered forecasting systems requires investment in sensors, data infrastructure, and advanced software platforms. Small and medium-sized farmers often struggle to afford these technologies, limiting adoption. Maintenance costs, including regular updates and technical support, add to the financial burden. Integration with existing farm management systems can also be complex and resource-intensive. These challenges slow penetration in cost-sensitive regions and among fragmented landholdings. Consequently, high costs remain a significant restraint to widespread adoption of AI-powered yield forecasting solutions.
Opportunity:
Rising need for optimized farm productivity
AI-powered forecasting enables farmers to plan planting schedules, irrigation, and harvesting with greater precision. This optimization reduces waste, enhances resource efficiency, and maximizes yields. As global food demand continues to rise, productivity improvements are critical to meeting supply requirements. AI solutions also support sustainable farming practices by minimizing environmental impact through data-driven decisions. Governments and agritech firms are increasingly promoting AI adoption to achieve food security and sustainability goals. As a result, the need for optimized productivity is expected to unlock substantial growth opportunities for the market.
Threat:
Dependence on quality and availability of data
Inaccurate or incomplete datasets can lead to unreliable predictions, undermining farmer confidence. Many regions lack robust data infrastructure, limiting the scope of AI applications. Seasonal variability and inconsistent weather records further challenge model accuracy. Data privacy concerns also restrict access to farm-level information, slowing adoption. Without high-quality inputs, AI systems cannot deliver the precision required for effective forecasting. Consequently, dependence on data availability remains a critical threat to market credibility and growth.
Covid-19 Impact:
The COVID-19 pandemic had a mixed impact on the AI-powered yield forecasting market. Supply chain disruptions delayed deployment of sensors and data infrastructure, slowing adoption in several regions. Farmers faced financial uncertainty, reducing investment in advanced technologies during the crisis. However, the pandemic highlighted the importance of resilience and efficiency in agriculture, driving renewed interest in predictive solutions. Remote monitoring and digital platforms gained traction as physical access to farms was restricted. Governments also emphasized food security, accelerating adoption of AI forecasting tools.
The polyethylene (PE) segment is expected to be the largest during the forecast period
The polyethylene (PE) segment is expected to account for the largest market share during the forecast period, driven by its widespread use in agricultural applications. PE films and coverings are integral to data collection systems, enabling controlled environments for accurate yield forecasting. Their durability, cost-effectiveness, and versatility make them the preferred material for protective and monitoring solutions. Farmers rely on PE-based infrastructure to support AI-driven sensors and imaging devices. The segment benefits from strong demand across both developed and emerging markets. Rising adoption of AI forecasting tools further reinforces the importance of PE materials in agricultural setups.
The transparent barrier films segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the transparent barrier films segment is predicted to witness the highest growth rate due to its role in enhancing data accuracy. These films allow optimal visibility for sensors and imaging devices, improving the precision of AI-powered forecasts. Their lightweight and flexible properties make them suitable for diverse agricultural applications. Rising demand for advanced monitoring solutions is accelerating adoption of transparent barrier films. Manufacturers are innovating with sustainable and high-performance materials to meet evolving needs. Integration with smart farming infrastructure further strengthens the segment’s growth trajectory.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share driven by advanced agricultural infrastructure. Farmers in the United States and Canada are leveraging predictive analytics to optimize yields and resource use. Strong government support and investment in agritech innovation reinforce regional leadership. The presence of leading AI firms and agricultural cooperatives accelerates commercialization of forecasting solutions. High awareness of sustainability and efficiency further strengthens demand. Retail and cooperative networks also facilitate widespread adoption of AI-powered tools.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR by rising food demand. Countries such as China, India, and Australia are increasingly adopting AI-powered forecasting to improve productivity. Expanding middle-class populations and government initiatives promoting smart farming support adoption. Farmers in the region are becoming more aware of the benefits of predictive analytics in managing risks. E-commerce and digital platforms are making AI solutions more accessible across diverse markets. Rising investment in agritech startups further accelerates regional growth.
Key players in the market
Some of the key players in AI-Powered Yield Forecasting Market include IBM, Microsoft, Google, Amazon Web Services, SAP SE, Oracle Corporation, Siemens AG, Deere & Company (John Deere), AG Leader Technology, Trimble Inc., Climate LLC, Granular (Corteva Agriscience), Prospera Technologies, Taranis and CropX Technologies.
Key Developments:
In May 2024, Microsoft announced major new AI and cloud capabilities within its Azure AI Services, including updates to Azure OpenAI Service. These enhancements empower developers and agri-tech companies to build more sophisticated predictive analytics tools on the Azure platform, directly improving the power and accessibility of AI-driven yield forecasting solutions for farmers.
In February 2023, IBM partnered with NASA to deploy its foundational AI model for geospatial data, aiming to vastly improve climate and agricultural analytics. This collaboration enhances the ability to predict crop yields by analyzing environmental factors like soil moisture and land use from satellite imagery with unprecedented accuracy, providing a powerful tool for the agricultural sector.
Film Types Covered:
• Metallized Barrier Films
• Transparent Barrier Films
• Co-Extruded Multilayer Films
• Laminated Films
• Other Film Types
Materials Covered:
• Polyethylene (PE)
• Polypropylene (PP)
• Polyethylene Terephthalate (PET)
• Ethylene Vinyl Alcohol (EVOH)
• Polyvinylidene Chloride (PVDC)
• Other Materials
Thicknesses Covered:
• Below 50 microns
• 50–100 microns
• 100–150 microns
• Above 150 microns
Packaging Formats Covered:
• Pouches
• Bags
• Lidding Films
• Wraps & Sheets
• Blisters
• Other Packaging Formats
Technologies Covered:
• Extrusion Coating
• Solvent-Based Coating
• Solvent-Free Coating
• Water-Based Coating
• Other Technologies
End Users Covered:
• Personal Care & Cosmetics
• Household Products
• Industrial Products
• Other End Users
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Table of Contents
200 Pages
- 1 Executive Summary
- 2 Preface
- 2.1 Abstract
- 2.2 Stake Holders
- 2.3 Research Scope
- 2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
- 2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions
- 3 Market Trend Analysis
- 3.1 Introduction
- 3.2 Drivers
- 3.3 Restraints
- 3.4 Opportunities
- 3.5 Threats
- 3.6 Technology Analysis
- 3.7 End User Analysis
- 3.8 Emerging Markets
- 3.9 Impact of Covid-19
- 4 Porters Five Force Analysis
- 4.1 Bargaining power of suppliers
- 4.2 Bargaining power of buyers
- 4.3 Threat of substitutes
- 4.4 Threat of new entrants
- 4.5 Competitive rivalry
- 5 Global AI-Powered Yield Forecasting Market, By Film Type
- 5.1 Introduction
- 5.2 Metallized Barrier Films
- 5.3 Transparent Barrier Films
- 5.4 Co-Extruded Multilayer Films
- 5.5 Laminated Films
- 5.6 Other Film Types
- 6 Global AI-Powered Yield Forecasting Market, By Material
- 6.1 Introduction
- 6.2 Polyethylene (PE)
- 6.3 Polypropylene (PP)
- 6.4 Polyethylene Terephthalate (PET)
- 6.5 Ethylene Vinyl Alcohol (EVOH)
- 6.6 Polyvinylidene Chloride (PVDC)
- 6.7 Other Materials
- 7 Global AI-Powered Yield Forecasting Market, By Thickness
- 7.1 Introduction
- 7.2 Below 50 microns
- 7.3 50–100 microns
- 7.4 100–150 microns
- 7.5 Above 150 microns
- 8 Global AI-Powered Yield Forecasting Market, By Packaging Format
- 8.1 Introduction
- 8.2 Pouches
- 8.3 Bags
- 8.4 Lidding Films
- 8.5 Wraps & Sheets
- 8.6 Blisters
- 8.7 Other Packaging Formats
- 9 Global AI-Powered Yield Forecasting Market, By Technology
- 9.1 Introduction
- 9.2 Extrusion Coating
- 9.3 Solvent-Based Coating
- 9.4 Solvent-Free Coating
- 9.5 Water-Based Coating
- 9.6 Other Technologies
- 10 Global AI-Powered Yield Forecasting Market, By End User
- 10.1 Introduction
- 10.2 Personal Care & Cosmetics
- 10.3 Household Products
- 10.4 Industrial Products
- 10.5 Other End Users
- 11 Global AI-Powered Yield Forecasting Market, By Geography
- 11.1 Introduction
- 11.2 North America
- 11.2.1 US
- 11.2.2 Canada
- 11.2.3 Mexico
- 11.3 Europe
- 11.3.1 Germany
- 11.3.2 UK
- 11.3.3 Italy
- 11.3.4 France
- 11.3.5 Spain
- 11.3.6 Rest of Europe
- 11.4 Asia Pacific
- 11.4.1 Japan
- 11.4.2 China
- 11.4.3 India
- 11.4.4 Australia
- 11.4.5 New Zealand
- 11.4.6 South Korea
- 11.4.7 Rest of Asia Pacific
- 11.5 South America
- 11.5.1 Argentina
- 11.5.2 Brazil
- 11.5.3 Chile
- 11.5.4 Rest of South America
- 11.6 Middle East & Africa
- 11.6.1 Saudi Arabia
- 11.6.2 UAE
- 11.6.3 Qatar
- 11.6.4 South Africa
- 11.6.5 Rest of Middle East & Africa
- 12 Key Developments
- 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
- 12.2 Acquisitions & Mergers
- 12.3 New Product Launch
- 12.4 Expansions
- 12.5 Other Key Strategies
- 13 Company Profiling
- 13.1 IBM
- 13.2 Microsoft
- 13.3 Google
- 13.4 Amazon Web Services
- 13.5 SAP SE
- 13.6 Oracle Corporation
- 13.7 Siemens AG
- 13.8 Deere & Company (John Deere)
- 13.9 AG Leader Technology
- 13.10 Trimble Inc.
- 13.11 Climate LLC
- 13.12 Granular (Corteva Agriscience)
- 13.13 Prospera Technologies
- 13.14 Taranis
- 13.15 CropX Technologies
- List of Tables
- Table 1 Global AI-Powered Yield Forecasting Market Outlook, By Region (2024-2032) ($MN)
- Table 2 Global AI-Powered Yield Forecasting Market Outlook, By Film Type (2024-2032) ($MN)
- Table 3 Global AI-Powered Yield Forecasting Market Outlook, By Metallized Barrier Films (2024-2032) ($MN)
- Table 4 Global AI-Powered Yield Forecasting Market Outlook, By Transparent Barrier Films (2024-2032) ($MN)
- Table 5 Global AI-Powered Yield Forecasting Market Outlook, By Co-Extruded Multilayer Films (2024-2032) ($MN)
- Table 6 Global AI-Powered Yield Forecasting Market Outlook, By Laminated Films (2024-2032) ($MN)
- Table 7 Global AI-Powered Yield Forecasting Market Outlook, By Other Film Types (2024-2032) ($MN)
- Table 8 Global AI-Powered Yield Forecasting Market Outlook, By Material (2024-2032) ($MN)
- Table 9 Global AI-Powered Yield Forecasting Market Outlook, By Polyethylene (PE) (2024-2032) ($MN)
- Table 10 Global AI-Powered Yield Forecasting Market Outlook, By Polypropylene (PP) (2024-2032) ($MN)
- Table 11 Global AI-Powered Yield Forecasting Market Outlook, By Polyethylene Terephthalate (PET) (2024-2032) ($MN)
- Table 12 Global AI-Powered Yield Forecasting Market Outlook, By Ethylene Vinyl Alcohol (EVOH) (2024-2032) ($MN)
- Table 13 Global AI-Powered Yield Forecasting Market Outlook, By Polyvinylidene Chloride (PVDC) (2024-2032) ($MN)
- Table 14 Global AI-Powered Yield Forecasting Market Outlook, By Other Materials (2024-2032) ($MN)
- Table 15 Global AI-Powered Yield Forecasting Market Outlook, By Thickness (2024-2032) ($MN)
- Table 16 Global AI-Powered Yield Forecasting Market Outlook, By Below 50 microns (2024-2032) ($MN)
- Table 17 Global AI-Powered Yield Forecasting Market Outlook, By 50–100 microns (2024-2032) ($MN)
- Table 18 Global AI-Powered Yield Forecasting Market Outlook, By 100–150 microns (2024-2032) ($MN)
- Table 19 Global AI-Powered Yield Forecasting Market Outlook, By Above 150 microns (2024-2032) ($MN)
- Table 20 Global AI-Powered Yield Forecasting Market Outlook, By Packaging Format (2024-2032) ($MN)
- Table 21 Global AI-Powered Yield Forecasting Market Outlook, By Pouches (2024-2032) ($MN)
- Table 22 Global AI-Powered Yield Forecasting Market Outlook, By Bags (2024-2032) ($MN)
- Table 23 Global AI-Powered Yield Forecasting Market Outlook, By Lidding Films (2024-2032) ($MN)
- Table 24 Global AI-Powered Yield Forecasting Market Outlook, By Wraps & Sheets (2024-2032) ($MN)
- Table 25 Global AI-Powered Yield Forecasting Market Outlook, By Blisters (2024-2032) ($MN)
- Table 26 Global AI-Powered Yield Forecasting Market Outlook, By Other Packaging Formats (2024-2032) ($MN)
- Table 27 Global AI-Powered Yield Forecasting Market Outlook, By Technology (2024-2032) ($MN)
- Table 28 Global AI-Powered Yield Forecasting Market Outlook, By Extrusion Coating (2024-2032) ($MN)
- Table 29 Global AI-Powered Yield Forecasting Market Outlook, By Solvent-Based Coating (2024-2032) ($MN)
- Table 30 Global AI-Powered Yield Forecasting Market Outlook, By Solvent-Free Coating (2024-2032) ($MN)
- Table 31 Global AI-Powered Yield Forecasting Market Outlook, By Water-Based Coating (2024-2032) ($MN)
- Table 32 Global AI-Powered Yield Forecasting Market Outlook, By Other Technologies (2024-2032) ($MN)
- Table 33 Global AI-Powered Yield Forecasting Market Outlook, By End User (2024-2032) ($MN)
- Table 34 Global AI-Powered Yield Forecasting Market Outlook, By Personal Care & Cosmetics (2024-2032) ($MN)
- Table 35 Global AI-Powered Yield Forecasting Market Outlook, By Household Products (2024-2032) ($MN)
- Table 36 Global AI-Powered Yield Forecasting Market Outlook, By Industrial Products (2024-2032) ($MN)
- Table 37 Global AI-Powered Yield Forecasting Market Outlook, By Other End Users (2024-2032) ($MN)
- Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
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