DEEP LEARNING FOR SOIL ANALYSIS AND PREDICTION IN PRECISION AGRICULTURE

Abstract

This paper explores the application of deep learning in soil analysis and prediction within the context of precision agriculture. Traditional soil analysis methods face limitations in terms of cost, time, and spatial resolution, prompting the adoption of deep learning techniques. Neural networks, inspired by the human brain, are employed to process extensive soil-related datasets, enabling efficient feature extraction and accurate predictions. The integration of deep learning in precision agriculture opens avenues for targeted interventions, optimizing crop yield, minimizing resource inputs, and enhancing sustainability. The paper discusses key components of deep learning for soil analysis and emphasizes its transformative potential in reshaping soil management practices.