Water salinity variability mapping for flooded paddy plots at Kuala Kedah, Malaysia

Salinity is an essential parameter in rice cultivation activity. It has a significant impact on paddy growth and also the yield of paddy. However, the level of salinity concentration in paddy plots depends on the surrounding conditions. The distance of the paddy area from the coastline, the temperature and intensity of rainfall should be considered in studies involving water quality. Additionally, the tide of events is also included in this study because of the position of the study area near the coastline. Therefore, this study was conducted to assess the level of salinity in two different rice cultivation seasons and describe the level of salinity concentration using a salinity variability map using the Inverse Distance Weighted (IDW) interpolation method. The data collection activities involved water sampling at 44 water inlets for each paddy plot in 30 hectares of the study area by referring to the Day After Sowing (DAS) as the paddy’s growth stage. These water samples were collected on 10 DAS, 40 DAS, and 60 DAS and subsequently tested using a portable conductivity meter namely EC500 Exstick II pH/Conductivity/Temperature Meter. Parallelly, georeference data which is latitude, longitude and elevation were gathered using Garmin GPSMAP 64s. Then, these data were analyzed using the IDW interpolation method in ArcGIS software and comes with salinity variability maps. The produced maps give an overview of the salinity concentration distribution by color scale range. Based on these salinity variability maps, the highest salinity concentration was recorded on 10 DAS and 60 DAS during Season 1 2019 and Season 2 2019, respectively for both tidal events. This result shows that the salinity concentration trend for both seasons is different due to the amount of rainfall received and the position of the paddy plot compared to the mean temperature factor

Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images

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