Soil Quality Prediction Using Deep Learning

Abstract

This research introduces a state-of-the-art method for predicting soil quality using deep learning algorithms. It utilizes a comprehensive dataset of physical, chemical, and biological parameters collected from multiple sites. The model combines long short-term memory (LSTM) networks to capture temporal relationships and convolutional neural networks (CNNs) to extract features from soil samples. Extensive experiments demonstrate the model's superiority over conventional machine learning methods, accurately predicting soil quality. The implications extend to sustainable land management, environmental monitoring, and precision agriculture. The CNN model achieves high accuracy in classifying soil quality and identifies influential variables. The study showcases its proficiency in predicting various soil measures and employs cross-validation to ensure consistency. This research contributes to the field by offering a precise and reliable approach to soil quality prediction, aiding decision-making in sustainable land management.