The report includes:
- Impact analysis of artificial intelligence (AI) on the agricultural biotechnology market
- Case studies of the successful integration of AI and machine learning in biotech seeds, biologicals, biotechnology tools and others
- Highlights of AI adoption trends across major geographical regions, as well as disruptions caused by AI in plants, animals and microbes
- Review of the impact of AI on various stages of the agricultural biotechnology market's value chain, along with a supply chain analysis
- Competitive landscape featuring key strategic initiatives, market spending and an investment outlook
Table of Contents
Chapter 1 Key Use Cases for AI Adoption in Agricultural Biotechnology- Overview
- Key Use Cases/Case Studies
- Case 1: Plants
- Case 2: Animals
- Case 3: Microbes
- Adoption Trends by Region
- Adoption Trends in North America
- Adoption Trends in Europe
- Adoption Trends in Asia-Pacific
- Adoption Trends in the Rest of the World
- Impact of AI on Key Industry Verticals
- Biotech Seeds
- Biologicals
- Biotechnology Tools
- Other Products
- Impact of AI on Various Stages of the Value Chain
- Discovery and Design
- Data Generation and Testing
- Data Modeling and Prediction
- Deployment and Optimization
- Overview
- Market Spending and Forecasting Analysis
- Key AI Spending for Agricultural Biotechnology
- Venture Capital Investment in AI for Agricultural Biotechnology
- Key VC Investments
- Abbreviations Used in the Report
Table 1: Key Initiatives by Companies on AI, 2025-2026
Table 2: Key Spending by Companies on AI-Based Agricultural Biotechnology, 2023-2026
Table 3: Abbreviations Used in This Report
List of Figures
Figure 1: Impact of AI on Value Chain

