Journal of Agricultural Science and Botany

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Research Article - Journal of Agricultural Science and Botany (2018) Volume 2, Issue 4

A new rapid, low-cost and GPS-centric unmanned aerial vehicle incorporating in-situ multispectral oil palm trees health detection.

Malaysia as the second largest producer of palm oil in the world has an industry that provides sizeable economic benefits for the country in term of healthy food and export opportunities. The greatest threat to oil palm (Elaeis guineensis) cultivation in Malaysia is from stem rot, caused by the white-rot fungus ganoderma boniness, which if neglected, may affect almost half a million hectares of oil palm farmland by the year 2020. This dreaded disease has no known effective cure and its spread can be curbed by early detection. It is inevitable that remote sensing techniques using multispectral or hyper spectral sensors be employed on unmanned aerial vehicles (UAV) to serve as an exploratory platform to identify the issues and scope involved for the synchronisation of different sensors, georeferencing the data acquired to avoid identification problems of tree identification in homogenous farmlands and the calculation of vegetation indices. The work is successful for its ability to screen each individual tree within almost three hectares of farmland within one minute of flight time, calculate the plant health indicator in real-time and dispense away with the cumbersome work routines to prepare for the acquisition of geolocation referential data mapping a homogenous farmland. Given the economic parameters, the enormous size of plantations, and the state of UAVs reliability, this research further give credence to the two-stage approach of using a lower cost multispectral imager to blanket-screen all trees individually and subsequently deploying a hyperspectral imager to confirm the health of suspect trees that were previously identified at the earlier stage. Post-processing of the data is still possible since the entire image is stored on the computing platform’s external storage.

Author(s): Bryan D. See*, Shaiful J. Hashim, Helmi Z. M. Shafri, Syaril Azrad, Mohd. Roshdi Hassan

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