[M-SD41] Geospatial applications for natural resources, environment and agriculture

[MSD41-P03] Rice growth monitoring using unmanned aerial vehicle and geographic information systems approaches

*Nik Norasma CheYa¹、Nur Adibah _ Mohidem¹、Muhd Syafie Asyraf Sarifudin¹、Abdul Syukor Juraimi²、Wan Fazilah Fazlil¹、Wan Nor Zanariah Zainol @ Abdullah³、Mohamad Husni Omar⁴ (1.Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.、2.Department of Crop Science, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Malaysia、3.Department of Science and Technology, Faculty of Humanities, Management and Science Universiti Putra Malaysia Bintulu Sarawak Campus, 97008 Bintulu, Sarawak, Malaysia、4.Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia)

キーワード：rice mapping, unmanned aerial vehicle , normalized difference vegetation index, weed detection, geographic information system

Rice as the main food source is significantly representing the national food security among citizens in Malaysia. Therefore, precision agriculture is frequently used to monitor paddy growth, yield prediction, and weed monitoring in the rice field. For example, Unmanned Aerial vehicles (UAVs) mounted with multispectral, hyperspectral or thermal sensors can detect the paddy’s unique electromagnetic signature for crop health monitoring and Geographic Information systems (GIS) can identify the presence of weed. Each band's electromagnetic signature reflectance value can be used to analyze the crop condition and identify the weeds in the field. This research aims to produce a paddy growth map based on Normalized Difference Vegetative Index (NDVI) value and generate a weed map. This study was carried out at the paddy field in Kota Bharu, Kelantan, Malaysia. The image data were collected using multispectral 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. The images were subjected to orthomosaic process and were embedded with information that indicated the health status of the rice crop. Image processing was used to identify the weeds in the field. The aerial image revealed that NDVI was able to visualize rice crop growth and identify the damaged area including weed in the paddy plot. Integration of UAV and GIS with NDVI approaches could help farmers to take immediate actions to counteract the damage in a shorter time period compared to conventional techniques. The paddy growth map could improve the efficiency of paddy monitoring and controlling the weeds in the field, which in turn increases the self-sufficiency level on rice production.