Intelligent microscopic approach for identification and recognition of citrus deformities

Abstract

Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial‐based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self‐collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

“…In the area of pattern recognition, features play a key role in the classification process such as object classification (Raza et al, ), human action classification (Aurangzeb et al, ), medical diseases classification (Iqbal et al, ; Saba, Mohamed, El‐Affendi, Amin, & Sharif, , Saba, Mohamed, El‐Affendi, Amin, & Sharif, ; Saba, Sameh, Khan, Shad, & Sharif, ), agriculture plants diseases recognition (Khan, Akram, Sharif, Awais, et al, 2018; Safdar et al, ), and many more (Khan et al, ; Sadad et al, ). In this work, we implement multilevel features extraction such as HOG, SURF, and color for the classification of skin lesions in the category of benign or malignant melanoma.…”

“…A lot of techniques are presented for contrast enhancement in this domain to improve the visual quality of lesion in the given images. Moreover, a few well-known segmentation methods available in computer vision are thresholding, K-means, Fuzzy-C means, and saliency Khan, Rashid, Sharif, Javed, & Akram, 2019;Safdar et al, 2019). The famous feature extraction techniques which are used for classification are ABCDE, HOG, point, texture, geometric, and many more (Ebrahim, Kolivand, Rehman, Rahim, & Saba, 2018;Sadad, Munir, Saba, & Hussain, 2018).…”

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

“…From the past few years, features extractions gain much attention in the area of pattern recognition based on several applications such as medical (Rehman et al, 2020), surveillance (M. Sharif, Akram, Raza, Saba, & Rehman, 2020a), agriculture (Adeel et al, 2019; Safdar et al, 2019), and many more (M. A. Khan, Javed, Sharif, Saba, & Rehman, 2019b; S. A. Khan et al, 2019). The main purpose of feature extraction is to analyze the patterns into a relevant category based on a few strong points.…”

Authentication through gender classification from iris images using support vector machine

“…On the other side, the iris has relatively simple imaging and high processing costs, while providing remarkable security. One of the most effective ways to reduce the system's computational complexity and response time is to reduce the number of comparisons (Husham, Hazim Alkawaz, Saba, Rehman, & Saleh Alghamdi, 2016; Saba, Rehman, Al‐Dhelaan, & Al‐Rodhaan, 2014; Safdar et al, 2019). Thus, Iris recognition is part of biometric recognition whereby the iris can be identified with a minimum number of comparisons.…”