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All Journal International Journal of Electrical and Computer Engineering
Srivastava, Devesh Kumar
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Computer Science & IT Electrical & Electronics Engineering

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SADCNN-ORBM: a hybrid deep learning model based citrus disease detection and classification Saini, Ashok Kumar; Bhatnagar, Roheet; Srivastava, Devesh Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2191-2201

Abstract

Citrus disease has a significant influence on agricultural productivity these days, so technology based on artificial intelligence has been developed for creating computer vision (CV) models. By spotting disease in its early stages and enabling necessary productivity actions, CV in agriculture improves the production of agricultural goods. In this paper, we developed a CV-based citrus disease detection model called the self-attention dilated convolutional neural network optimized restricted Boltzmann machine (SADCNN-ORBM) model, which consists of two crucial parts: a SADCNN for disease segmentation and an ORBM optimized by the self-adaptive coati optimization (SACO) algorithm to improve the classification performance of diseases, which successfully divides the disease type into three groups: anthracnose, melanose, and brown spot. Numerous feature sets, such as texture features, three-channel red, green, blue (RGB) features, local binary pattern (LBP) features, and speeded-up robust features (SURF) features, are combined and given as input into the classification layer in the proposed model. We compare our proposed model's performance with existing methods by using several evaluation metrics. The findings demonstrate the SADCNN-ORBM model's superiority in precisely recognizing and classifying citrus illnesses, outperforming all available techniques.
Noise reduction in Hyperion high dynamic range hyperspectral data using machine learning and statistical techniques Nair, Priyanka; Srivastava, Devesh Kumar; Bhatnagar, Roheet
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6913-6928

Abstract

Numerous remote sensing applications rely heavily on hyperspectral imagery, but it is frequently plagued by noise, which degrades the data quality and hinders subsequent analysis. In this research paper, we present an in-depth analysis of noise removal techniques for hyperspectral imagery, specifically for data acquired from the Hyperion EO-1 sensor. Setting off with obtaining Hyperion data and the pre-processing stages, the paper discusses the acquisition and denoising of Hyperion data. The hyperspectral data considered is in the high dynamic range (HDR) format, which maintains the original imagery's complete dynamic range. The study explores various noise reduction methods, such as minimum noise fraction (MNF), principal component analysis (PCA), wavelet denoising, non-local means (NLM), and denoising autoencoders, aimed at enhancing the signal-to-noise ratio. The effectiveness of these techniques is evaluated through visual quality, mean square error (MSE), and peak signal-to-noise ratio (PSNR), alongside their impact on mineral exploration. Furthermore, the paper investigates the application of machine learning algorithms on denoised data for mineral identification, highlighting the potential of integrating denoising techniques with machine learning for improved mineral exploration. This comparative analysis aims to identify the most efficient noise removal methods for hyperspectral imagery, facilitating higher quality data for subsequent analysis.
Co-Authors Bhatnagar, Roheet Nair, Priyanka Saini, Ashok Kumar