Machine Learning in Agriculture: A Land Data Approach to Optimize Crop Choice with the LAGNet Model

Adopting machine learning in agriculture is essential for improving crop selections and increasing crop yield. Traditional crop selection methods sometimes rely on limited data, local knowledge, and outdated practices. These conventional techniques might result in inefficient crop selections, resource wastage, and reduced yields. In response to these limitations, introduce the Land Allocation and Crop Growth Network (LAGNet). This machine-learning model revolutionizes crop choice by integrating land and weather data with season-specific recommendations. The StudyStudy compares the LAGNet model to traditional techniques. The results show considerable increases in crop selection accuracy, with an average yield gain of 16%. LAGNet's powerful machine learning algorithms exceed conventional approaches, providing improved accuracy, sensitivity, specificity, and a higher F1 score. The LAGNet model delivers a complete and adaptable crop selection solution, empowering farmers with data-driven recommendations and eventually improving agricultural production, sustainability, and food security. The StudyStudy highlights LAGNet's transformational potential and role in modernizing precision agriculture.