Weed detection in rice fields using UAV and multispectral aerial imagery † - Rosle, Rhushalshafira; Sulaiman, Nursyazyla; Che’Ya, Nik Norasma; Mohd Radzi, Mohd Firdaus; Omar, Mohamad Husni; Berahim, Zulkarami; Fazlil Ilahi, Wan Fazilah; Shah, Jasmin Arif; Ismail, Mohd Razi (MDPI, 2022)
Weeds are plants that compete for nutrients, space, and light and exert many harmful effects by reducing the quality and quantity of crops if the weed population is uncontrolled. The direct yield loss has been estimated to be 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 negatively affect 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 UAVs and multispectral imagery solves the weed detection problem in a paddy field. The objective of this study project is to detect weeds in rice fields using a UAV and multispectral imagery. Multispectral imagery was used to identify the condition of the crops. It can be an indicator to determine weeds and paddy plants based on the spectral resolution in the imagery. This study was performed at Tunjang, Jitra, Kedah, which has a total area of 0.5 ha. The two types of data collections of this study are 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, an unmanned aerial vehicle (UAV) was used, attached with a multispectral camera, Micasense, and a Red Green Blue (RGB) camera. Aerial data collection was conducted on the same day as ground data collection, on 30 June 2020 (the day after sowing (DAS) 34). A correlation between these two data was conducted. The study output is a weed map developed from the RGB image and multispectral imagery normalized difference vegetation index (NDVI) map. The correlation of the NDVI value with the UAV with SPAD data was weak. It has a positive, but not significant.