AI-Driven Soil Classification: Faster R-CNN Deep Learning Approach: Achieving 99.94% accuracy in soil type detection using Faster R-CNN deep learning architecture - Softcover

Mondal, Saptarshi; Roy, Rupsha

 
9786208454180: AI-Driven Soil Classification: Faster R-CNN Deep Learning Approach: Achieving 99.94% accuracy in soil type detection using Faster R-CNN deep learning architecture
Softcover
ISBN 10: 6208454182 ISBN 13: 9786208454180
Verlag: LAP LAMBERT Academic Publishing, 2025

Inhaltsangabe

This book presents an advanced deep learning solution for soil classification using Faster R-CNN, achieving 99.94% accuracy. It leverages image-based analysis to accurately classify multiple soil types, including Black, Alluvial, Loamy, and Red soils. The approach integrates image preprocessing, region proposal networks, and robust neural feature extraction to ensure high detection and classification performance. Visual outputs, including bar charts, scatter plots, and line graphs, illustrate predictive accuracy and confidence scores, enabling a better understanding of model performance. Designed for applications in precision agriculture and environmental science, this work reduces dependency on traditional lab-based soil analysis and speeds up decision-making in soil management. By merging AI-driven techniques with practical agricultural needs, this research sets a benchmark for soil analytics and highlights how deep learning can transform sustainable farming and resource optimization.

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Über die Autorin bzw. den Autor

Saptarshi Mondal, a B.Tech CSE (AIML) 3rd-year student at Adamas University, has published a Springer paper on AI for disabled assistance. Rupsha Roy, a B.Sc (Hons) Agriculture 3rd-year student at Adamas University, specializes in climate-resilient farming, focusing on sustainable agriculture solutions.

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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2025
Sprache
Englisch
ISBN 10 
6208454182
ISBN 13 
9786208454180
Einband
Taschenbuch
Anzahl der Seiten
56
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

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