Fungal Disease Detection in Maize Leaves Using Haar Wavelet Features

Deshapande, Anupama S.; Chickerur, Satyadhyan; Karibasappa, K.; Desai, Shrinivas D.

doi:10.1007/978-981-13-1742-2_27

Cited by 32 publications

(8 citation statements)

References 10 publications

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“…Classification is a step for grouping features based on similarity or proximity. Various classical machine learning methods are used for classification such as Naïve Bayes [4], [30], [32], Decision Tree [4], [27], [30], [32], k-Nearest Neighbor [4], [33], Support Vector Machine with all its variants [4]- [6], [23], [25], [26], [29]- [35], Random Forest [4], [30], [32], Deep Forest [4], [36]. Neural Network [22], [24], [25], [32], and Bag of Features [6].…”

Section: Classificationmentioning

confidence: 99%

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.

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Section: Classificationmentioning

confidence: 99%

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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“…However, the technique failed to consider other conditions like image capturing modes, geographical areas, and cultivation conditions. Deshapande, A.S et al [4]presented a model for finding disease from the maize leaf disease. The model aimed at earlier detection of diseases and classifies the disease.…”

Section: Literature Surveymentioning

confidence: 99%

Adams Algorithm with Generative Adversial Network for Maize Leaf Disease Classification

Irfan¹

2022

JNACS

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The diseases in plant revealed overwhelming bang on providing security in food production and may cause diminution in quality of agriculture-assisted items. In several scenarios, the disease in plant led to no grain produce. Hence, automated detection of plant disease is suggested for discovering the information regarding agriculture. Various methods are developed to discover plant disease wherein deep model is mostly favoured. A new model is devised for maize leaf disease classification. Here, the pre-processing is a preliminary step to eradicate noise contained in image and implemented with ROI extraction and anisotropic filter. Thereafter, the saliency map extraction is adapted for computing the quality of each pixel. The purpose of saliency map extraction is to alter representation of image into something which is more meaningful or easy to inspect. Finally, the classification of maize leaf disease is executed with Generative Adversial network (GAN), and helps to classify whether the maize leaf is normal or infectious. The GAN training is implemented with Adam. The Adam GAN showed best performance with improved accuracy of 0.811, sensitivity of 0.879 and specificity of 0.939.

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“…The country's position helps to explain its mild winters and hot, dry summers. It is between 15.2 and 32.6degrees north latitude and 34.1 and 55.5degrees [1] east longitude. Outside of the southwestern highlands, where it rains more often in the summer, air receives less than 100 millimetres of precipitation annually.…”

Section: Introductionmentioning

confidence: 99%

“…Fig.3shows the pepper leaf images of the proposed work. Pepper leaf pictures, as well as unaffected input tomato, are taken into consideration for the input image, which is indicated by the letter I, and the impacted area of interest (ROI) is written as follows,(1) …”

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confidence: 99%

A Framework for Agriculture Plant Disease Prediction using Deep Learning Classifier

Baljon

2023

IJACSA

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The agricultural industry in Saudi Arabia suffers from the effects of vegetable diseases in the Central Province. The primary causes of death documented in this analysis were 32 fungal diseases, two viral diseases, two physiological diseases, and one parasitic disease. Because early diagnosis of plant diseases may boost the productivity and quality of agricultural operations, tomatoes, Pepper and Onion were selected for the experiment. The primary goal is to fine-tune the hyperparameters of common Machine Learning classifiers and Deep Learning architectures in order to make precise diagnoses of plant diseases. The first stage makes use of common image processing methods using ml classifiers; the input picture is median filtered, contrast increased, and the background is removed using HSV color space segmentation. After shape, texture, and color features have been extracted using feature descriptors, hyperparameter-tuned machine learning (ML) classifiers such as k-nearest neighbor, logistic regression, support vector machine, and random forest are used to determine an outcome. Finally, the proposed Deep Learning Plant Disease Detection System (DLPDS) makes use of Tuned ML models. In the second stage, potential Convolutional Neural Network (CNN) designs were evaluated using the supplied input dataset and the SGD (Stochastic Gradient Descent) optimizer. In order to increase classification accuracy, the best Convolutional Neural Network (CNN) model is fine-tuned using several optimizers. It is concluded that MCNN (Modified Convolutional Neural Network) achieved 99.5% classification accuracy and an F1 score of 1.00 for Pepper disease in the first phase module. Enhanced GoogleNet using the Adam optimizer achieved a classification accuracy of 99.5% and an F1 score of 0.997 for Pepper illnesses, which is much higher than previous models. Thus, proposed work may adapt this suggested strategy to different crops to identify and diagnose illnesses more effectively.

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Fungal Disease Detection in Maize Leaves Using Haar Wavelet Features

Cited by 32 publications

References 10 publications

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Adams Algorithm with Generative Adversial Network for Maize Leaf Disease Classification

A Framework for Agriculture Plant Disease Prediction using Deep Learning Classifier

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