eBook overview
This page gives the clean, canonical details for this title: what it covers, who it is for, and where to get it.
Agriculture
AI in Agriculture: Revolutionizing Farming for a Sustainable Future
Artificial intelligence transforms agriculture with precision farming, crop monitoring, and predictive analytics, enhancing yields and sustainability. Pages: 330.
330
Agriculture
Artificial Intelligence
AI Trends
Who it is for
Readers who want practical, plain-English AI analysis without the usual marketing confetti cannon.
Why it matters
This title focuses on applied AI, real-world trade-offs, and what actually matters once the hype has left the room.
Quick facts
Length
330 pages
Primary lens
Agriculture
Best for
Farm owners, agritech teams, sustainability leads, policymakers, and readers who want to understand how AI changes decisions in the field rather than in a pitch deck.
Related AI topics
This book sits comfortably alongside broader explainers and sector-specific guides.
Why it matters
Agriculture lives or dies on tight margins, ugly weather, supply shocks, and timing. AI helps when it improves forecasting, resource use, and early detection — not when it pretends to replace agronomy.
What you’ll learn
- How precision agriculture uses sensors, drones, and prediction models.
- Where crop monitoring, yield forecasting, and irrigation optimisation deliver real value.
- Why data quality, equipment cost, and local conditions still make or break results.
What this book covers
- Precision farming and variable-rate inputs
- Crop health monitoring and pest detection
- Supply chain planning, weather modelling, and sustainability trade-offs
What makes this book distinct
It treats agriculture as an operational system with weather, soil, labour, and machinery constraints — not as a generic “AI use case”.
Get this book
Artificial intelligence transforms agriculture with precision farming, crop monitoring, and predictive analytics, enhancing yields and sustainability. 330-page guide.
Deeper overview
Longer-form context from the retired overview page, now folded into the canonical book route.
Problem framing: where this topic gets messy
Agriculture runs on weather, timing, labour, machinery, and margins. AI matters when it sharpens those decisions, not when it turns into another expensive gadget story. Artificial intelligence transforms agriculture with precision farming, crop monitoring, and predictive analytics, enhancing yields and sustainability. Pages: 333. This title focuses on applied AI, real-world trade-offs, and what actually matters once the hype has left the room.
Practical outcomes
In practical terms, the aim is simple: you should finish with a clearer picture of where prediction, monitoring, and optimisation can genuinely help farmers and agritech teams. That means clearer judgement, fewer lazy assumptions, and a much better sense of where to press further or walk away.
- Understand how precision agriculture uses sensors, drones, and prediction models.
- Identify where crop monitoring, yield forecasting, and irrigation optimisation deliver real value.
- Grasp why data quality, equipment cost, and local conditions still make or break results.
Chapter-level signals
Not a chapter list carved in stone, but the sort of material readers can reasonably expect to work through.
Precision farming and variable-rate inputs
Precision farming and variable-rate inputs.
Crop health monitoring and pest detection
Crop health monitoring and pest detection.
Supply chain planning, weather modelling, and
Supply chain planning, weather modelling, and sustainability trade-offs.
What makes this title distinct
It treats agriculture as an operational system with weather, soil, labour, and machinery constraints — not as a generic “AI use case”.
FAQ
What will I learn from this book?
Agriculture lives or dies on tight margins, ugly weather, supply shocks, and timing. AI helps when it improves forecasting, resource use, and early detection — not when it pretends to replace agronomy. It treats agriculture as an operational system with weather, soil, labour, and machinery constraints — not as a generic “AI use case”.
Who is this book for?
Farm owners, agritech teams, sustainability leads, policymakers, and readers who want to understand how AI changes decisions in the field rather than in a pitch deck.
How long is it?
It’s 330 pages (varies by edition).
Where should I go next after reading it?
Use the related links on this page, then jump to the AI in Business guide or the wider ebook catalogue.
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Use the links below to carry on browsing the wider catalogue, the podcast, the newsletter, or a related topic.