Enhancing Rice Crop Monitoring Through UAV Imagery And GIS Analysis

Abstract Unmanned aerial vehicles (UAVs) are becoming increasingly important in many industries, and agriculture is no exception. This study aimed to monitor rice fields by using UAV-based imagery to map rice fields thoroughly and using on-the-ground Soil Plant Analysis Development (SPAD) data to evaluate crop health. A series of UAV-captured photos were analyzed as part of the inquiry, and the results included the creation of reclassification maps and Normalized Difference Vegetation Index (NDVI), which were reinforced by GIS analysis. The results show that during the 11-day post-sowing phase, a portion of the crop area has low vegetation value, with 44% showing lower vegetation values in the range of 0 to 0.32. During the vegetative stages, the NDVI trend shows a progressive increase from non-vegetation to lower vegetation values. Particularly, the NDVI score increases during the booting and blooming stages, reaching its maximum at 74 days after sowing (DAS) at around 53% coverage for higher vegetation. An examination of data from ground samples reveals a positive connection (r=0.7696) between the mean NDVI values and SPAD measurements. The research findings indicate that analysing the series of UAV images enables the evaluation of vegetation health through the examination of NDVI value trends across multiple flight sessions. This approach is valuable reference for identifying specific areas of interest to facilitate effective monitoring.