A Pso Model for Disease Pattern Detection on Leaf Surfaces

Muthukannan, K.; Latha, P.

doi:10.5566/ias.1227

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…Aduwo et al [45] devised an automated vision-based analysis system to detect cassava mosaic disease, using color and shape features and various classifiers, including the Naive Bayes classifier. Muthukannam et al [46] emphasized the significance of Particle Swarm Optimization (PSO)-based image segmentation for plant leaf disease identification. Jian et al [47] introduced a method for identifying diseases in cucumber leaves using Support Vector Machines (SVM) with polynomial, radial basis function, and sigmoid kernel functions.…”

Section: A Traditional Methodsmentioning

confidence: 99%

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

Satya Rajendra Singh,

Sanodiya

2023

IEEE Access

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Agriculture's pivotal role in sustaining livelihoods and driving economic growth is widely recognized, yet various challenges like the adverse effects of climate change and limited resource availability hinder its productivity. Notably, plants are susceptible to various viruses and bacteria, impacting yield and food security. The emergence of deep learning, particularly convolutional neural networks (CNNs), has transformed agriculture by facilitating tasks such as disease detection. However, a significant challenge arises from the often unrealistic assumption that training and testing data share the same distribution. To address this, domain adaptation and transfer learning techniques have been employed, bridging the gap between different data distributions. Therefore, a novel framework named 'Zero-Shot Transfer Learning' is introduced. This addresses the challenge of improving classifier performance when trained on a source domain with different classes and tested on a target domain, exemplified by tomato and potato datasets. More specifically, in this framework, we include different CNN models along with techniques such as data augmentation, synthetic data generation, and robust discriminative losses, enhancing classifier performance in zero-shot scenarios. Extensive experiments on plant leaf disease classification under the zero-shot Transfer Learning assumption demonstrate the superiority of the proposed framework for effective disease classification. Ultimately, this framework holds the potential to promote crop yield optimization and ensure food security.

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Section: A Traditional Methodsmentioning

confidence: 99%

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

Satya Rajendra Singh,

Sanodiya

2023

IEEE Access

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“…Finally, in the last step, texture analysis was performed on useful segments using color co-occurance matrix to recognize the plant diseases. Latha (2015). PSO is a self-regulating, efficient unsupervised technique for better segmentation and feature extraction.…”

Section: Related To Plant Disease Recognition Using Dip Techniquesmentioning

confidence: 99%

Classification Models for Plant Diseases Diagnosis: A Review

Uppal

Hai

Tewari

2022

IJRITCC

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Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved.

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“…Lastly, the derived features were processed through neural network model. Muthukannan and Latha [16] proposed a novel solution to image segmentation, called PSO. PSO is an efficient, selfregulating unsupervised algorithm that is used for improved segmentation and extraction of features.…”

Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Yadav¹,

Akanksha²,

Yadav³

2020

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Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

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A Pso Model for Disease Pattern Detection on Leaf Surfaces

Cited by 13 publications

References 4 publications

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

Classification Models for Plant Diseases Diagnosis: A Review

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

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