DOI : https://doi.org/10.18517/ijaseit.6.4.898

Rice Yield Estimation Using Below Cloud Remote Sensing Images Acquired by Unmanned Airborne Vehicle System

C.C Teoh (1), N Mohd Nadzim (2), M.J Mohd Shahmihaizan (3), I Mohd Khairil Izani (4), K Faizal (5), H.B. Mohd Shukry (6)
(1) Engineering Research Centre, Malaysian Agricultural Research and Development Institute P.O. Box 12301, 50774 Kuala Lumpur, Malaysia
(2) Engineering Research Centre, Malaysian Agricultural Research and Development Institute P.O. Box 12301, 50774 Kuala Lumpur, Malaysia
(3) Engineering Research Centre, Malaysian Agricultural Research and Development Institute P.O. Box 12301, 50774 Kuala Lumpur, Malaysia
(4) Engineering Research Centre, Malaysian Agricultural Research and Development Institute P.O. Box 12301, 50774 Kuala Lumpur, Malaysia
(5) Engineering Research Centre, Malaysian Agricultural Research and Development Institute P.O. Box 12301, 50774 Kuala Lumpur, Malaysia
(6) Engineering Research Centre, Malaysian Agricultural Research and Development Institute P.O. Box 12301, 50774 Kuala Lumpur, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
Teoh, C.C, et al. “Rice Yield Estimation Using Below Cloud Remote Sensing Images Acquired by Unmanned Airborne Vehicle System”. International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 4, July 2016, pp. 516-9, doi:10.18517/ijaseit.6.4.898.
Citation Format :
A method using unmanned airborne vehicle system (UAVS) and image processing technique to enable estimation of rice yield was developed. A digital Tetracam camera was mounted on a CropCam unmanned airborne vehicle (UAV) to acquire red (R), green (G) and near infrared (NIR) images of rice crops at the height of 300 m above ground.  NIR and R values were used to calculate normalised difference vegetation index (NDVI) value. Relationships between yield versus R, G, NIR and NDVI values were analysed. Results showed that the highest relationship was found in NDVI followed by R, G and NIR with coefficient of determination (r2) values of 0.748, 0.727, 0.395 and 0.014 respectively. Therefore, a yield estimation model using NDVI value was developed from the linear regression analysis. The results showed that the model was capable of estimating rice yield with an average accuracy value of 80.3%.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).