Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
AI-Driven Soil Classification: Faster R-CNN Deep Learning Approach

AI-Driven Soil Classification: Faster R-CNN Deep Learning Approach

Mondal, Saptarshi
Roy, Rupsha

Paperback

Botany & Horticulture

ISBN10: 6208454182
ISBN13: 9786208454180
Publisher: LAP Lambert Academic Publishing
Published: Aug 6 2025
Pages: 56
Weight: 0.19
Height: 0.13 Width: 6.00 Depth: 9.00
Language: English
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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