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

arXiv: https://arxiv.org/abs/2105.04356 · HTML: https://mlsanity.com/paper/2105.04356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Improving Generalization Performance by Switching from Adam to SGD](https://mlsanity.com/paper/1712.07628/) (2017)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://mlsanity.com/paper/1912.02801/) (2019)
- [Crop Planning using Stochastic Visual Optimization](https://mlsanity.com/paper/1710.09077/) (2017)

## Built on by (most cited first, 2 of 2)

- [Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning](https://mlsanity.com/paper/2507.19199/) (2025)
- [Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation](https://mlsanity.com/paper/2505.15077/) (2025)

## Abstract (arXiv)

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 is presented for the detection and segmentation of coconut tress in aerial imagery provided through the AI competition organized by the World Bank in collaboration with OpenAerialMap and WeRobotics. Maked Region-based Convolutional Neural Network approach was used identification and segmentation of coconut trees. For the segmentation task, Mask R-CNN model with ResNet50 and ResNet1010 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.