Rubber disease mapping using low altitude multispectral images

Abstract

One of the most important rubber leaf diseases in Malaysia is Oidium leaf disease. A Severe outbreak of this disease may cause an annual yield loss with 20% decrement of latex production in a rubber plantation. Currently, data acquisition is obtained manually which results in lack effectiveness, costly, and typically labour incentive. However, recent technology using unmanned aerial vehicle (UAV) has potential to overcome these issues. The UAV is able to facilitate spatially and allow temporal flexible data acquisition using a compact camera payload. Therefore, this study intention is to identify the health of rubber tree that affected by Oidium leaf disease using low altitude remote sensing images that acquired from UAV platform. The study area is carried out at Experimental Rubber Plot, Research Station Malaysian Rubber Board, Kota Tinggi, Johor in order to generate the healthy, unhealthy and severe map. Those maps were produced using multiple regression analysis between spectral reflectance at blue (450 – 510 nm), green (530 – 590 nm), red (640 – 670 nm) and near-infrared (850 – 880 nm) based on UAV. Oidium leaf disease can be identified by low absorption of light at the red band and little at the near infrared band resulted of high reflectance at both bands. As a result, this study successfully to determine the condition of rubber tree caused by leaf disease using low-cost remote sensing technology which deploys the UAV and payload by the digital compact camera.