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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing

Youseef Alotaibi¹, Brindha Rajendran², Geetha Rani K³

¹College of Computer and Information Systems, Umm Al Qura University, Makkah, Saudi Arabia.

Peerj. Computer Science

|March 4, 2024

Summary

This summary is machine-generated.

A new method, Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC), significantly improves crop classification accuracy using remote sensing images. This advancement aids food security and environmental monitoring.

Keywords:

Crop classification Deep learning Dipper throat optimization algorithm Image processing Remote sensing images

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Remote Sensing
Agricultural Science
Computer Science

Background:

Advancements in remote sensing necessitate efficient crop classification for food security and environmental monitoring.
Traditional methods struggle with accuracy and scalability for high-resolution remote sensing data.
Accurate crop classification is crucial for sustainable agriculture and resource management.

Purpose of the Study:

To develop a novel crop classification technique, Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC).
To enhance the accuracy and efficiency of crop classification from remote sensing images.
To achieve high classification accuracy for diverse food crops.

Main Methods:

Utilized a GoogleNet architecture (Deep Convolutional Neural Network - DCNN) for feature extraction.
Employed Dipper Throated Optimization (DTO) for hyperparameter tuning of the GoogleNet model.
Used Extreme Learning Machine (ELM) for crop classification, with parameters fine-tuned by the Modified Sine Cosine Algorithm (MSCA).

Main Results:

The DTODCNN-CC approach demonstrated significantly higher crop classification accuracy.
Experimental analyses confirmed the superior performance compared to existing state-of-the-art deep learning methods.
The optimized GoogleNet and ELM models achieved robust feature extraction and classification.

Conclusions:

DTODCNN-CC offers a promising solution for accurate and efficient crop classification using remote sensing data.
This technique has substantial potential for applications in agriculture, food security, and environmental monitoring.
The study highlights the effectiveness of integrating optimization algorithms with deep learning for remote sensing applications.