.png){kind=icon}
.png){kind=icon}
Return
Artificial plant optimization algorithm to detect infected leaves using machine learning
.png){kind=icon}
PRE
AI
DOI：10.1111/exsy.12501.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
convolutional neural network
histogram of oriented gradients
improved artificial plant optimization
machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.3
Papers:
2.5K
Citations:
3.8K