Advancing Sugarcane Farm Management Through NDVI-Based Color Mapping and Drone Imaging

Abstract

Precision agriculture has emerged as a key strategy for boosting crop productivity and optimizing resource use. This study leverages advanced imaging and machine learning to enhance the management of sugarcane farms. Using drones, high-resolution RGB images of sugarcane fields are captured and transformed into multispectral images through a Generative Adversarial Network (GAN), revealing critical spectral data for plant health assessment. The Normalized Difference Vegetation Index (NDVI) is derived from these multispectral images and serves as a vital measure of vegetation health. This NDVI data, combined with farmer-reported yield information, creates a comprehensive dataset linking NDVI values to actual crop yields. To predict sugarcane yield from NDVI values, we trained a feedforward neural network on this integrated dataset. The proposed method not only enhances prediction accuracy but also provides valuable insights into the connection between NDVI metrics and crop performance. The model was validated using individual field images, enabling precise yield predictions for different field sections. This study highlights the effectiveness of integrating drone imagery, machine learning, and remote sensing in precision agriculture. The combination of NDVI data with yield information provides a robust tool for optimizing sugarcane production, improving farm management decisions, and advancing agricultural sustainability.