Machine Learning for Precision Agriculture: A Practical Guide to Python, Remote Sensing, GIS and AI
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Description
Machine Learning for Precision Agriculture is a practical guide to building models that hold up. It teaches the whole workflow, from satellite imagery and yield monitor files through to a prescription file a machine can execute, and it is unusually direct about what does not work. What you will learn Why random cross-validation is invalid on agricultural data, and how to split by field, site and season instead Cleaning yield monitor data properly, and why a global outlier filter deletes your best ground Sentinel-2, NDVI and the vegetation indices, including where NDVI saturates and what to use after it does Google Earth Engine and QGIS workflows for field-scale analysis Delineating management zones, and how to prove the zones are worth having Why a yield map cannot produce a fertiliser rate, and what can Deep learning for crop imagery, and the many cases where a simpler model wins Turning a prediction into a decision that survives contact with an agronomist Inside 28 chapters across 245 pages, plus four reference appendices 60 Python listings, checked against the libraries they call 10 end-to-end projects, specified as briefs with explicit success criteria 40 interview questions with model answers 155 practice multiple-choice questions, each with a worked explanation 14 original figures, a glossary, and a guide to dataset licences Who it is for Agronomists moving into data science. Data scientists moving into agriculture. Students and researchers who need a workflow that survives review. Consultants who have to defend a number to the person paying for it. You need to be able to read Python. You do not need a GPU, a cloud budget, or a background in remote sensing. What this book will not do It will not hand you a model that predicts yield anywhere on Earth, or an accuracy figure detached from how it was measured. Neither exists. What it gives you is a defensible workflow, an honest account of uncertainty, and the judgement to recognise when the honest answer is that the data cannot support the question. Book one of the GeoAG Insights precision agriculture series. Read more
Publisher : Independently published
Publication date : August 3, 2026
Language : English
Print length : 245 pages
ISBN-13 : 83
Item Weight : 1.2 pounds
Dimensions : 7.5 x 0.56 x 9.25 inches
Best Sellers Rank: #2,173,009 in Books (See Top 100 in Books) #1,062 in Scientific Research #28,060 in Science & Mathematics
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