Disease Identification in Plant Leaf Using Deep Convolutional Neural Networks

Venu, K.; Palanisamy, Natesan; Krishnakumar, B.; Sasipriyaa, N.

doi:10.4018/978-1-5225-9902-9.ch003

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Guo et al [8] reported accuracy rates of 94.0 percent, 86.7 percent, 88.8 percent, and 84.4 percent, respectively, for the identification of downy mildew, anthracnose, powdery, and grey mould infections. Venu et al [9] developed a neural network-based pattern recognition method to pinpoint the most common tomato late blight disease on a global scale. In the present approach of 20 networks, the best prediction level for pixel classification was 97.99 percent.…”

Section: Ai Based Phenomics Approach In Disease Identificationmentioning

confidence: 99%

An Overview on Phenomics Applications in Different Agriculture Disciplines

Hilli

2022

IJPSS

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In the past ten years, ground and aerial platforms with several sensors have been rapidly adopted for phenotyping diverse biotic, abiotic stressors and other characters during the crop plant's growth stages. An increase in yield achieved from traditional breeding programs worldwide are no longer sufficient to meet the projected demand for the three major cereals like Rice, Wheat and Maize. Thus the phenomics application which includes high throughput phenotyping (HTP) i.e artificial intelligence based techniques should be bought over traditional phenotyping in order to meet the needy demands of traditional ones. Also Artificial intelligence (AI)-based data analysis techniques can increase the reliability of diagnoses and, as a result, be included into instruments for effective treatment. These methods find pertinent data for use in plant breeding and pathology activities by using feature extraction, identification, classification, and prediction criteria as well as for precision breeding. This approach has various applications in various agriculture disciplines. The main steps under such techniques includes: Image acquisition, Preprocessing of images, segmentation of images, Image Representation and Description and Image recognition. The use of these helps to speed up genetic progress and lessen the phenotyping drawbacks.

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Section: Ai Based Phenomics Approach In Disease Identificationmentioning

confidence: 99%

An Overview on Phenomics Applications in Different Agriculture Disciplines

Hilli

2022

IJPSS

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“…The algorithm inspired by the diverse structure of the human brain; the Artificial Neural Network (ANN) has been used in numerous complex problems [7]. It can process a large volume of data, learn from training data, and excellent generalization capability [8]. Due to its capability of self-learning and self-adapting, more research on this algorithm has been successfully implemented to solve real-world problems.…”

Section: Introductionmentioning

confidence: 99%

Students’ learning habit factors during COVID-19 pandemic using multilayer perceptron (MLP)

Mangshor¹,

Ibrahim²,

Sabri³

et al. 2021

IJATEE

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Rapid dissemination of coronavirus disease 2019 (COVID-19) across the globe has necessitated the introduction of social distance interventions to slow the spread of the disease. Online learning has become essential, considering the implications of this virus to be spread among the students during physical classes. Hence, educational institutions have shifted the traditional physical classes to online classes. Due to this implementation worldwide, a study on student learning habits is crucial to analyse students learning habits as it is one of the main factors that affecting students' performance in learning. Fifteen independent variables as inputs to one of the well-known Artificial Neural Network algorithms, Multilayer Perceptron (ANN-MLP) has been used to investigate the student's learning habit factors during the COVID-19 pandemic. Through analysing original survey data from 420 secondary students (Grade 6-12) in Hanoi shows that the ANN-MLP model is stable for both ANN-MLP optimization algorithms which are for Adjusted Normalized, to be concise. The hours spend for self-learning before COVID-19 is observed to be the most influential factors of student's learning habit during COVID-19 pandemic. Moreover, the promising Sum of Squares Error (SSE) and Relative Error (RE) values obtained signify that the ANN-MLP model is appropriate in identifying the student's learning habit factors during COVID-19 pandemic.

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“…The Artificial Neural Network (ANN) has the ability to learn from experiences, improving its performance and adapting to the changes in the environment [2]. The key benefits of neural networks are the prospect of processing vast quantities of data effectively, and their ability to generalize outcomes [3]. The ANNs are parallel distributed systems, consisting of simple processing units (artificial neurons) that measure with certain (usually nonlinear) [4] mathematical functions.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF): COVID-19 Spread and Death Contributing Factors

Ibrahim¹

2020

IJATCSE

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The Artificial Neural Network (ANN) is an Artificial Intelligence technique which has the ability to learn from experiences, enhancing its performance by adapting to the environmental changes. The key benefits of neural networks are the prospect of processing vast quantities of data effectively, and their ability to generalize outcomes. Considering the great potential of this technique, this paper aims to establish a performance evaluation of Multilayer Perceptron (MLP) and a Radial Basis Function (RBF) networks in investigating the contributing factors for COVID-19spread and death. The RBF and MLP networks are typically used in the same form of applications, however, their internal calculation structures are different. A comparison was made by using a dataset of COVID-19 cases in 41 Asia countries during April 2020. There are nine contributing factors which acted as the covariates to the network such as Cases, Deaths, High Temperature, Low Temperature, Population, Percentage of Cases over Population, and Percentage of Death over Population, Average Temperature, and Total Cases. The results obtained from the testing sets indicated that the two neural structures were able to investigate the COVID-19 spread and death contributing factors. Nevertheless, the RBFnetwork indicated a slightly better performance than the MLP.

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Disease Identification in Plant Leaf Using Deep Convolutional Neural Networks

Cited by 4 publications

References 5 publications

An Overview on Phenomics Applications in Different Agriculture Disciplines

An Overview on Phenomics Applications in Different Agriculture Disciplines

Students’ learning habit factors during COVID-19 pandemic using multilayer perceptron (MLP)

Performance Evaluation of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF): COVID-19 Spread and Death Contributing Factors

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