Articoli correlati a AI-Driven Soil Classification: Faster R-CNN Deep Learning...

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 - Brossura

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
Brossura
ISBN 10: 6208454182 ISBN 13: 9786208454180
Casa editrice: LAP LAMBERT Academic Publishing, 2025

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Editore
LAP LAMBERT Academic Publishing
Data di pubblicazione
2025
Lingua
Inglese
ISBN 10 
6208454182
ISBN 13 
9786208454180
Rilegatura
Copertina flessibile
Numero di pagine
56
Contatto del produttore
non disponibile
Persona responsabile
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Germania