Deep learning model BiFPN-YOLOv8m for tree counting in mango orchards using satellite remote sensing data | JoVE Visualize

Area of Science:

Agricultural Science
Remote Sensing
Computer Vision

Background:

Mango production is economically vital for India, necessitating accurate tree inventory for yield assessment.
Traditional tree counting methods are labor-intensive, costly, and prone to errors.
Satellite Remote Sensing offers a scalable solution for ecological and agricultural parameter estimation.

Purpose of the Study:

To develop and evaluate a deep learning model for high-resolution, image-based mango tree counting.
To compare the performance of the proposed Bi-directional Feature Pyramid Network (BiFPN)-YOLOv8m against various YOLOv8 variants and other state-of-the-art methods.
To enhance the accuracy and efficiency of mango tree inventory for improved agricultural planning and crop yield forecasting.

Main Methods:

Utilized a dataset of 1700 training and 300 testing high-resolution satellite images of mango orchards.
Implemented and evaluated multiple object detection models including YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x), YOLOv9, YOLOv10, and the proposed BiFPN-YOLOv8m.
Focused on assessing models based on computational efficiency, accuracy, and speed for tree counting.

Main Results:

The proposed BiFPN-YOLOv8m model demonstrated superior performance in accurately locating and counting mango trees.
The model achieved state-of-the-art results, outperforming other evaluated methods, even in challenging environmental conditions.
Experimental findings highlighted the model's effectiveness in separating and counting trees within orchards.

Conclusions:

Deep learning, specifically the BiFPN-YOLOv8m model, provides a highly effective and efficient solution for image-based mango tree counting.
The developed approach significantly overcomes the limitations of conventional methods, offering improved accuracy for ecological and agricultural applications.
This technology holds substantial promise for advancing precision agriculture, environmental monitoring, and crop yield prediction in mango cultivation.