Deep Learning-Based Crop Prediction Using LSTM And GRU For Sustainable Agriculture

Abstract

The rapid development of technology in agriculture has opened the door to data-driven solutions to increase crop productivity and sustainability. This paper introduces a machine learning-based method for crop prediction through deep learning models such as long short-term memory (LSTM) and gated recurrent units (GRU). By exploiting environmental factors such as soil content, temperature, moisture, and nutrient levels, the suggested model accurately suggests the most suitable crops for a specific area. Preprocessing operations such as feature scaling, principal component analysis (PCA), and one-hot encoding are applied to the dataset derived from Kaggle, which enhance the prediction effectiveness. Experimental results reflect the model’s ability to optimize crop choices and, subsequently, reduce the associated risks from unknown climate changes, soil degradation, and less efficient use of resources. This work highlights the impact of deep learning in agricultural decision-making that supports higher productivity and sustainability.

Index Terms—Crop Prediction, Machine Learning, Deep Learning, LSTM, GRU, Precision Agriculture, Sustainable Farm- ing, Climate Resilience, Resource Optimization, AI in Agricul- ture.