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

Mondal, Saptarshi; Roy, Rupsha

ISBN 10: 6208454182 / ISBN 13: 9786208454180

Published by LAP LAMBERT Academic Publishing, 2025

About this Item

Seller Inventory # I-9786208454180

Bibliographic Details

• Title: 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
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6208454182
• ISBN 13: 9786208454180
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.