Advancing Soil Fertility Classification: Comparative Analysis of Machine Learning Models

Main Article Content

Anshu Bhasin, Kavita Paurh

Abstract

Soil fertility detection is crucial for sustainable agriculture and environmental management. This study proposes an innovative approach utilizing advanced Convolutional Neural Networks (CNNs) integrated with an entropy-based method for accurate soil fertility assessment. The proposed methodology involves the acquisition of soil images through sensors or drones, followed by preprocessing to enhance image quality and reduce noise. Subsequently, a deep CNN architecture is employed to extract high-level features from the soil images, enabling automated detection of fertility indicators such as nutrient levels and soil texture. Furthermore, an entropy-based approach is incorporated to analyze the spatial distribution of features within the images, providing additional insight into soil heterogeneity and fertility variation. The combination of CNNs and entropy-based analysis offers a comprehensive solution for precise soil fertility detection, surpassing traditional methods in accuracy and efficiency. Experimental results demonstrate the effectiveness of the proposed approach, showcasing its potential for real-world application in precision agriculture, environmental monitoring, and land management. Overall, this research contributes to the advancement of intelligent systems for sustainable soil management and agricultural productivity enhancement.

Article Details

Section
Articles