NIPES Journal of Science and Technology Research 4(4) 2022 pp.40- 56 

Oil Palm Plantation Detection in Satellite Image Using Deep Learning

Fidelis Odinma Chete  and  Vincent Akinwande


The traditional approach, such as manual surveys, used by farmers and governments to detect where oil palms are planted has proven to be ineffective, tedious and time consuming. This problem can be addressed if images obtained from satellites can be used to scan through the forest to detect oil palm plantations.  In this paper, we developed a web based deep learning system that is capable of processing a satellite image of land to detect the presence of oil palm plantations in Nigeria using high-resolution satellite imagery. This research designed a detection system which uses a convolutional neural network to extricate important features, and a classifier trained using satellite images. Results showed exceptional effectiveness with a training loss of 0.11 and an accuracy of 99.0%. Utilizing different images for validation taken from diverse elevations, the model reached a training loss of 0.245 and an accuracy of 82.9999% on validation data, while on test data we got 1.59 in loss and an accuracy of 87.5%. Thus, the proposed approach is seemingly effective within the field of precision agriculture.

Keywords: Computerized Mapping, Convolutional Neural Network, Deep Learning, Oil Palm, Satellite Imagery

Cite this article as: Fidelis Odinma Chete, & Vincent Akinwande. (2022). Oil Palm Plantation Detection in Satellite Image Using Deep Learning. NIPES Journal of Science and Technology Research, 4(4), 40–56.

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