IJBTT

Hybrid Features based Classification of Insect and Leaf Disease of Soybean Plants using Random Forest Classifier

Hybrid Features based Classification of Insect and Leaf Disease of Soybean Plants using Random Forest Classifier
	© 2023 by IJETT Journal
	Volume-71 Issue-2
	Year of Publication : 2023
	Author : Chandrakant Mahobiya, Sailesh Iyer, Savita Kolhe
	DOI : 10.14445/22315381/IJETT-V71I2P242

How to Cite?

Chandrakant Mahobiya, Sailesh Iyer, Savita Kolhe, "Hybrid Features based Classification of Insect and Leaf Disease of Soybean Plants using Random Forest Classifier," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 408-420, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P242

Abstract
With the growth in the global population, agricultural productivity must expand. Since insects (pests) and crop diseases are among the difficulties farmers encounter, they can cause significant agricultural loss. It is critical to creating solutions that reduce losses in order to boost production. Some of these technologies are environmentally friendly, such as those designed for the automated and early diagnosis of diseases using image processing techniques in conjunction with deep learning computational algorithms. In India, more than 40 species of insect pests of this crop were registered, one of the most relevant being Blue beetle adult/ mrl Larvae/ mrl G.gemma Larvae/mrl, .acuta, Heliothis, Grey weevil adult/ mrl, Stem fly incidence % Plant inf./ mrl Girdle, beetle. The primary goal of this research is to explore the accuracy and efficiency of computational approaches used in the problem of soybean leaf disease detection and insect classification, which are implemented with the utilization of hybrid features. Soybean insect and leaf diseases automatic classification and the prediction model are presented with hybrid features created by extracting deep features from a convolutional neural network (CNN) and texture features (Acquired from Gabor Wavelet and Harris Corner Method). The proposed hybrid features are then classified by a random forest classifier. MATLAB-based simulations exhibit the performance for insects and disease detection and classification.

Keywords
Convolutional Neural Network, Deep learning, Gabor wavelet, Harris corner, Random forest classifier, Soybean.

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