Transfer learning-based deep ensemble neural network for plant leaf disease detection

Vallabhajosyula, Sasikala; Venkatramaphanikumar, S.; Kolli, Venkata Krishna Kishore

doi:10.1007/s41348-021-00465-8

Cited by 160 publications

(56 citation statements)

References 30 publications

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“…The approach of ensemble averaging combines the estimated probability of several base models [48]. Ensemble learning seeks to integrate several algorithms and merge the outcomes with various voting processes in order to outperform any one algorithm [49,50]. The base models used in this study are InceptionResNetV2, Xception, DenseNet201, and VGG19.…”

Section: Ensemble Tl Modelsmentioning

confidence: 99%

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Sharma¹,

Nath

Sharma

et al. 2022

Electronics

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Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

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Section: Ensemble Tl Modelsmentioning

confidence: 99%

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Sharma¹,

Nath

Sharma

et al. 2022

Electronics

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show abstract

“…In NasNet, although the architecture is predetermined, the number of cells or blocks to be found by the search method, that is, the number of initial convolution filters, are free parameters. [19].…”

Section: Nasnet-mobile Network Structurementioning

confidence: 99%

“…Coronavirus disease (COVID- 19), which emerged toward the end of the 2019, is a type of coronavirus known to infect extremely rapidly. The International Committee on Virus Taxonomy has named this virus SARS-COV-2 [1,2].…”

Section: Introductionmentioning

confidence: 99%

Application with deep learning models for COVID-19 diagnosis

Turk

KÖKVER

2022

Sakarya University Journal of Computer and Information Sciences

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COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged. The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

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“…Vallabhajosyula et al [45] çalışmasında RBB-1 ve RBB-2 yapılarına ait formülleri aşağıdaki şekilde ifade etmiştir.…”

Section: Mobilenetv2unclassified

Classification of Citrus Diseases with Convolutional Neural Network Based Deep Learning Model

ÇETİNER

Çetiner

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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ÖzTarımsal üretimde mahsul verimi için bitki hastalıkları hayati öneme sahiptir. Bitkilerde bulunan renk, şekil, doku gibi özelliklerin birbirine benzemesinden dolayı bitkilerdeki hastalıkların erken aşamada tespiti zor ve yorucu olmaktadır. Bitkilerdeki hastalıkların erken aşamada tespit edilerek önlem alınması mahsule gelen zararın engellemesi için gerekli bir adımdır. Bu nedenle, narenciye ihracatını etkileyerek üreticileri maddi olarak büyük zararlara uğratan yaprak hastalıklarını sınıflandırmak için yapılan çalışma kapsamında derin öğrenme tabanlı bir model geliştirilmiştir. Buna ek olarak DenseNet121, MobileNetV2 ve ResNet50 modellerini temel alan üç ayrı model ile de yaprak hastalıkları sınıflandırılmıştır. Bu modellerin oluşturulmasında ince ayarlı transfer öğrenme tekniği kullanılmıştır. Yapılan çalışma kapsamında önerilen 15 katmanlı CNN modeli ile Adamax optimizasyon yöntemi ile %99, RMSProp optimizasyon yöntemi ile de %97 doğruluk oranlarına ulaşılmıştır. En sık karşılaşılan narenciye yaprak hastalıklarından olan Blackspot (narenciye siyah nokta (CBS)), canker (narenciye bakteriyel kanseri (CBC)), greening (huanglongbing (HLB)) ile Health (sağlıklı (Health)) sınıflarında ise sırasıyla %100, %100, %98 ve %100 başarı oranlarına erişilmiştir.

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Transfer learning-based deep ensemble neural network for plant leaf disease detection

Cited by 160 publications

References 30 publications

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Application with deep learning models for COVID-19 diagnosis

Classification of Citrus Diseases with Convolutional Neural Network Based Deep Learning Model

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