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A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery.

Amr Abozeid¹, Rayan Alanazi¹, Ahmed Elhadad¹

¹Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi Arabia.

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|January 25, 2022

Summary

Researchers developed SwinTUnet, a deep learning model for accurately detecting and counting olive trees in satellite images. This method addresses challenges in computer vision for agricultural monitoring, improving precision farming and land management.

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Area of Science:

Computer Vision
Remote Sensing
Agricultural Technology

Background:

Olive trees hold significant economic and cultural value, with Saudi Arabia's Al-Jouf region recognized for the world's largest olive tree population.
Accurate olive tree detection and counting in satellite imagery is crucial for agricultural management but faces challenges like scale variation, weather, and perspective distortions.
Existing deep learning applications lack a standardized olive tree dataset, hindering research and development.

Purpose of the Study:

To develop a robust deep learning model for detecting and counting olive trees from satellite imagery.
To create a large-scale, specialized dataset of olive trees for deep learning applications.
To address the limitations of current computer vision techniques in agricultural remote sensing.

Main Methods:

Construction of a large-scale olive tree dataset comprising 230 RGB images from Al-Jouf, KSA.
Proposal of SwinTUnet, an efficient deep learning model based on a Unet-like architecture with Swin Transformer blocks.
Implementation of encoder, decoder, and skip connections to learn local and global semantic information for tree detection.

Main Results:

The proposed SwinTUnet model demonstrated superior performance in olive tree detection compared to existing methods.
Achieved an estimation error of 0.94% in overall detection accuracy on the custom-built dataset.
The SwinTUnet model effectively learns both local and global features crucial for accurate tree identification.

Conclusions:

SwinTUnet offers an efficient and accurate solution for detecting and counting olive trees in satellite imagery.
The developed dataset provides a valuable resource for advancing deep learning research in agricultural remote sensing.
This work contributes to improved precision agriculture and land management through advanced computer vision techniques.