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Crop Yield Forecasting and Acreage Estimation using Python: A Practical Guide with Remote Sensing & Machine Learning
Crop Yield Forecasting and acreage Estimation using Python is a comprehensive, hands-on guide that explains how modern agriculture can leverage satellite remote sensing, NDVI analysis, weather data, and machine learning to estimate crop area and predict yield before harvest.This book focuses on practical implementation rather than heavy theory, making it ideal for readers who want to build real-world crop analytics systems using Python. Each chapter walks step-by-step through the complete workflow — from satellite data preprocessing and crop mask generation to machine learning–based crop classification, yield forecasting, validation, and operational deployment.
Key topics covered in this book of remote sensing for agriculture
NDVI time-series analysis and vegetation monitoring
Crop mask generation and preprocessing using Python
Ground truth data preparation and feature extraction
Crop classification using Random Forest and machine learning
Crop yield forecasting using NDVI and weather data
Accuracy assessment, validation, and reporting standards
Automation, deployment, and decision support systems
The book is designed for students, researchers, GIS professionals, data scientists, and agricultural analysts who want a clear and applied understanding of crop Acreage estimation and yield forecasting. All workflows are explained using Python-based examples that can be adapted to different regions and crops.No advanced prior knowledge of remote sensing is required. By the end of the book, readers will be able to design, implement, and deploy end-to-end crop monitoring and forecasting solutions suitable for research, government projects, and operational agricultural applications
Key topics covered in this book of remote sensing for agriculture
NDVI time-series analysis and vegetation monitoring
Crop mask generation and preprocessing using Python
Ground truth data preparation and feature extraction
Crop classification using Random Forest and machine learning
Crop yield forecasting using NDVI and weather data
Accuracy assessment, validation, and reporting standards
Automation, deployment, and decision support systems
The book is designed for students, researchers, GIS professionals, data scientists, and agricultural analysts who want a clear and applied understanding of crop Acreage estimation and yield forecasting. All workflows are explained using Python-based examples that can be adapted to different regions and crops.No advanced prior knowledge of remote sensing is required. By the end of the book, readers will be able to design, implement, and deploy end-to-end crop monitoring and forecasting solutions suitable for research, government projects, and operational agricultural applications
63 pages, Kindle Edition
Published January 10, 2026
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