Paddy cultivation in Malaysia plays a crucial role in food production, with a focus on improving crop quality and quantity. With current national self-sufficiency levels ranging between 67 and 70%, the Malaysian government intends to produce higher-quality crops and boost agricultural production. However, the prominent paddy-producing state of Kedah has witnessed a decline in yields over the years. To address this, the study explores the effectiveness of unmanned aerial vehicles (UAVs) equipped with vegetation indices (VIs) for monitoring paddy plant health at various growth stages. Researchers acquired aerial imagery during two seasons in 2019, capturing three distinct growth stages: tillering (40 days after sowing), flowering (60 days after sowing), and ripening (100 days after sowing). These stages represent critical points in the paddy plant's life cycle. Agisoft Metashape software processed the images to extract VIs data. The study found that the Normalized Difference Vegetation Index (NDVI) and Blue Normalized Difference Vegetation Index (BNDVI) exhibited over 90% similarity. In contrast, the Normalized Difference Red Edge Index (NDRE), utilizing near-infrared and red-edge light reflections, demonstrated a unique relationship. NDRE outperformed NDVI and BNDVI with an R-squared value of 0.842, showcasing its superior accuracy, especially for dense crops like paddy plants sensitive to subtle changes in vegetation. In conclusion, this research highlights the potential of UAV-based VIs for effectively monitoring paddy plant health during different growth stages. The NDRE index, in particular, proves valuable for assessing dense crops, offering insights for precision agriculture and crop management in Malaysia.
