Artificial plant optimization algorithm to detect infected leaves using machine learning

Plant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) a...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems 2021-09, Vol.38 (6), p.n/a
Main Authors: Gupta, Deepak, Sharma, Prerna, Choudhary, Krishna, Gupta, Kshitij, Chawla, Rahul, Khanna, Ashish, Albuquerque, Victor Hugo C. de
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification
convolutional neural network
Datasets
Environmental impact
Feature extraction
histogram of oriented gradients
Histograms
Impact analysis
improved artificial plant optimization
Leaves
Machine learning
Medical imaging
Optimization
Plant diseases
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Plant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private dataset of 236 images. Features are extracted from the images using histogram of oriented gradients (descriptor). The concepts of artificial plant optimization are then applied to study the features of healthy leaves using IAPO. A machine learning algorithm has been created to make the model adaptive with varied datasets. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. The proposed IAPO is also compared with other classification algorithms including k‐nearest neighbours, support vector machine, random forest and convolution neural network that show accuracies of 78.24%, 83.48%, 87.83%, and 91.26%, respectively, whereas IAPO shows quite accurate results in classification of leaves with an accuracy of 97.45% on training set and 95.0% accuracy on test set.
ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.12501