Vision Transformer-Based Soil NPK Classification Using Infrared Heatmap Analysis and Optimization Techniques
Year: 2025
Type: article
Abstract: Accurate soil nutrient analysis is essential for optimizing fertilizer application and improving agricultural productivity. Traditional machine learning (ML) and deep learning (CNN-based) approaches have been widely used for soil classification; however, they face limitations in capturing long-range dependencies and complex feature representations. This study proposes a Vision Transformer (ViT)-based model for NPK classification from infrared heatmap images. The ViT architecture leverages self-attention mechanisms to enhance spatial feature extraction, improving classification accuracy. The experimental evaluation demonstrates that the proposed ViT model achieves a 94.2% classification accuracy, outperforming standard CNN architectures such as VGG19, ResNet-50, Inception-V3, MobileNet-V3, and EfficientNet-B2. The confusion matrix analysis highlights the model's robustness in distinguishing varying soil nutrient compositions, even under different moisture levels and fertilizer concentrations. The results validate the effectiveness of attention-driven feature extraction and optimization techniques in soil nutrient classification. This research establishes a strong foundation for precision agriculture, enabling real-time NPK monitoring and adaptive fertilizer management.
Author: Aarti Abhijit Chavan
Institution: Shivaji University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409430883