Application of multispectral UAV for paddy growth monitoring in Jitra, Kedah, Malaysia

Abstract

Rice is the staple food for most people in Southeast Asia, mainly Malaysia. Unfortunately, Malaysia does not reach a 100% self-sufficiency level on rice production due to inefficiency of rice farm management, pest and disease outbreak, poorly irrigation system, and climate change. Each spectral band of electromagnetic signature in the rice crops can be identified to analyse the crop condition based on the reflectance value. Therefore, unmanned aerial vehicle (UAV) can capture different spectral band images of the rice field depending on the sensors used. This study aims to produce a paddy growth map based on the normalized difference vegetative index (NDVI) value and validate the paddy growth map using the soil plant analysis development (SPAD) data. This study was carried out at the paddy field planted with PadiU Putra rice variety in Muda Agricultural Development Authority (MADA), Jitra in Kedah. Three reading samples for each point at the paddy field within 1 m radius were recorded. Then, the samples from each point were scanned using SPAD chlorophyll meter. The image data were collected using multispectral and RGB cameras at the altitude of 60 m, and a calibrated reflectance panel was used to calibrate the image. Ground control point (GCP) was placed at the four corners of the study plot, and it was being used as a georeferencing point for aerial imagery mapping. Those images were undergone orthomosaic process to produce a single overlapped image. NDVI was used to measure the healthy level of rice crops. NDVI map had shown the distribution of NDVI value across the study plot, which includes the healthy and less healthy vegetative area. SPAD value has no significant relationship with the aerial imagery of NDVI value. The NDVI map allows the farmers to monitor the paddy growth status and effectively improve their rice farm management. In the future, advanced classification methods based on the reflectance of weed, water, and soil can be prioritized and separated into different classes, whereby the NDVI map can be plotted on the paddy crops.