Weeds are plants that compete for nutrients, space, light and exert a lot of harmful effects by reducing the quality and quantity of crops if the weed population is left uncontrolled. The direct yield loss was estimated within the range of 16-86% depending on the type of rice culture, weed species, and environmental conditions. Currently, farmers apply herbicides at the same rate to control weeds. Excessive chemical usage will be negative effects on the environment, crop productivity, and the economy. A map-based system can help in directing the herbicide sprayer to specific areas. Producing a weed map is very challenging due to the similarity of the crops and the weeds. Therefore, using UAV and multispectral imagery will solve the weed detection problem in the paddy field. The objective of this project is to detect weeds in the rice field using UAV and multispectral imagery. Multispectral imagery was used to identify the condition of the crops, and it can be an indicator to determine weeds and paddy plants based on the spectral resolution in the imagery. This study was done at MADA, Tunjang, Jitra, Kedah with the total area used of 0.5 ha. 2 types of data collections were done, which were ground data and aerial data collection. Ground data were collected using the Soil Plant Analysis Development (SPAD) meter, which can read the chlorophyll value of the area. For aerial data, the unmanned aerial vehicle (UAV), attached with the multispectral camera, Micasense, and Red Green Blue (RGB) camera. Aerial data collection was collected on the same day as ground data collection, which on 30th June 2020 (day after sowing (DAS) 34). Correlation between those two data were made. The study output were a weed map developed from the RGB image and a normalized difference vegetation index (NDVI) map using multispectral imagery. The correlation of the NDVI value from the UAV with SPAD data was weak. It has a positive but no significant value at the 0.05 level (2 tail).