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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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Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning

Mojtaba Dadashzadeh¹, Yousef Abbaspour-Gilandeh¹, Tarahom Mesri-Gundoshmian¹

¹Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.

Plants (Basel, Switzerland)

|May 1, 2020

Summary

This study introduces a stereo vision system using artificial neural networks (ANNs) and metaheuristic algorithms to accurately distinguish rice from weeds. The developed system achieved high accuracy, improving eco-friendly weed management in rice cultivation.

Keywords:

eco-friendly technique metaheuristic algorithm rice field site-specific management sustainable agriculture weed

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Area of Science:

Agricultural Engineering
Computer Vision
Artificial Intelligence

Background:

Site-specific weed management in dense crops like rice is challenging.
Selective herbicide application requires accurate plant and weed discrimination.
Current methods often lack the precision needed for effective, eco-friendly weed control.

Purpose of the Study:

To develop a stereo vision system for distinguishing rice plants from weeds.
To further discriminate between two specific types of weeds within a rice field.
To enhance weed management strategies through advanced image analysis and classification.

Main Methods:

Recorded stereo videos in a rice field and processed frames.
Extracted green plants from the background after pre-processing and segmentation.
Utilized artificial neural networks (ANNs) optimized with particle swarm optimization (PSO) and the bee algorithm (BA) for feature selection and classification.
Extracted 302 color, shape, and texture features for discrimination.

Main Results:

The proposed ANN-BA classifier achieved high accuracies (88.74% right, 87.96% left channels).
Accuracies improved to 92.02% (arithmetic mean) and 90.7% (geometric mean).
The ANN-BA classifier significantly outperformed the K-nearest neighbors (KNN) classifier, which had lower overall accuracy.

Conclusions:

The developed stereo vision system effectively distinguishes rice from weeds with high accuracy.
The integration of ANNs and metaheuristic algorithms offers a promising solution for site-specific weed management.
This technology can contribute to more sustainable and eco-friendly agricultural practices in rice cultivation.