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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop

Ahmed S Almasoud¹, Hanan Abdullah Mengash², Muhammad Kashif Saeed³

¹Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.

Biomimetics (Basel, Switzerland)

|November 24, 2023

Summary

This summary is machine-generated.

This study introduces a new method using marine predators algorithm with deep learning for food crop classification from remote sensing images. The approach enhances accuracy in identifying crop types, outperforming existing deep learning techniques.

Keywords:

computer vision crop classification deep learning machine learning remote sensing images

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Area of Science:

Agricultural Remote Sensing
Artificial Intelligence in Agriculture
Machine Learning for Crop Classification

Background:

Remote sensing (RS) data from UAVs and satellites are increasingly used for agricultural applications like crop classification.
Traditional classification methods struggle with heterogeneous crop planting, necessitating advanced AI techniques.
Deep learning (DL) offers superior feature extraction for effective crop type detection.

Purpose of the Study:

To develop a novel remote sensing imagery data analysis technique for food crop classification.
To enhance the accuracy and generalization capabilities of crop classification models.
To investigate the effectiveness of the Marine Predators Algorithm (MPA) combined with DL for this task.

Main Methods:

Designed the Remote Sensing Marine Predators Algorithm with Deep Learning for Food Crop Classification (RSMPA-DLFCC) technique.
Utilized the SimAM-EfficientNet model for feature extraction from RS images.
Employed the MPA for optimal hyperparameter selection to enhance the SimAM-EfficientNet architecture and used an Extreme Learning Machine (ELM) for classification.

Main Results:

The RSMPA-DLFCC technique effectively analyzes RS data to determine food crop varieties.
MPA optimization significantly improved the classification accuracy and generalization of the SimAM-EfficientNet model.
Simulation analysis on benchmark datasets demonstrated superior performance compared to existing DL techniques.

Conclusions:

The proposed RSMPA-DLFCC technique offers a robust and accurate solution for food crop classification using remote sensing data.
The integration of MPA with DL models provides an effective strategy for optimizing agricultural classification tasks.
This approach holds significant potential for improving crop monitoring, yield estimation, and land management.