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Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network


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Iqbal, MS, Ali, H, Tran, SN ORCID: 0000-0002-5912-293X and Iqbal, T 2021 , 'Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network' , IET Computer Vision, vol. 15, no. 6 , pp. 428-439 , doi: 10.1049/cvi2.12028.

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Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in the disaster-hit areas. In this article, a deep learning approach was presented for the detection and segmentation of coconut trees in aerial imagery provided through the AI competition organised by the World Bank in collaboration with OpenAerialMap and WeRobotics. Masked Region-based Convolution Neural Network (Mask R-CNN) approach was used for identification and segmentation of coconut trees. For the segmentation task, Mask R-CNN model with ResNet50 and ResNet101 based architectures was used. Several experiments with different configuration parameters were performed and the best configuration for the detection of coconut trees with more than 90% confidence factor was reported. For the purpose of evaluation, Microsoft COCO dataset evaluation metric namely mean average precision (mAP) was used.An overall 91% mean average precision for coconut trees’ detection was achieved.

Item Type: Article
Authors/Creators:Iqbal, MS and Ali, H and Tran, SN and Iqbal, T
Journal or Publication Title: IET Computer Vision
Publisher: Wiley-Blackwell Publishing Ltd
ISSN: 1751-9632
DOI / ID Number: 10.1049/cvi2.12028
Copyright Information:

© 2021 The Authors. IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, ( which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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