Leaf identification based on back propagation neural network and support vector machine

Ankalaki, Shilpa; Majumdar, Jharna

doi:10.1109/ccip.2015.7100736

Cited by 6 publications

(1 citation statement)

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“…The HOG is a feature descriptor, which describes appearance of local object by the distribution of intensity gradients. An RGB (Red, Green and Blue) histogram of leaf image was converted into its single-channel grayscale equivalent (Ankalaki & Majumdar, 2015) by using Equation ( 8):…”

Section: Image Preprocessing: Feature Extraction Using Hogmentioning

confidence: 99%

Artificial plant optimization algorithm to detect infected leaves using machine learning

et al. 2020

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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. K E Y W O R D Sconvolutional neural network, histogram of oriented gradients, improved artificial plant optimization, machine learning 1 | INTRODUCTION Data explosion is observed everywhere these days as data created in terms of volume in the past few years are colossal than in the history of human race. Thus, it has become important to find optimal solutions that can help in managing these large amounts of data. Intelligent bio-inspired algorithms are introduced to deal with incompleteness and imperfectness of dynamic problems (Fister, Brest, Fister, & Yang, 2015). These algorithms are called intelligent as they can acclimatize and behave like real world organisms. But most of the bio-inspired algorithms are getting ignored due to rigorous development in this field.The following are some important and prevalent pieces of work done in the field of bio-inspired algorithms. Artificial plant optimization algorithm (APOA) was used by Ziqiang, Zhihua and Jianchao (Zhao, Cui, Zeng, & Yue, 2011) to solve constrained optimization problem in which they proposed a shrinkage coefficient for checking the feasible region of a particle.

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Section: Image Preprocessing: Feature Extraction Using Hogmentioning

confidence: 99%