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

Sharma, Mayuri; Nath, Keshab; Sharma, Rupam; Kumar, Chitranjan; Chaudhary, Ankit

doi:10.3390/electronics11010148

Cited by 43 publications

(17 citation statements)

References 42 publications

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“…Where n indicates the inputs to the neuron, Wi is weight and Xi is the value associated with the ith input, and ‘b’ is the bias term. It computes weighted sum of the inputs, adds a bias term, and then applies the activation function to produce the output of the neuron [ 19 , 20 ]. B) Convolutional neural networks (CNN) …”

Section: Materials and Methods Usedmentioning

confidence: 99%

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

et al. 2023

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Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

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Section: Materials and Methods Usedmentioning

confidence: 99%

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

et al. 2023

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“…On the public dataset, the proposed approach obtained an average identification accuracy of 99.67%. Sharma, Nath, et al (2022) studied the issue of the resource-constrained embedded system used in smart agriculture. To recognize the deficiencies in rice plants, they use six different transfer learning architectures, such as DenseNet201, ResNet50V2, Xception, InceptionResNetV2, InceptionV3, and VGG19.…”

Section: Related Workmentioning

confidence: 99%

SE_SPnet: Rice leaf disease prediction using stacked parallel convolutional neural network with squeeze‐and‐excitation

et al. 2023

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Rice is one of the significant crops, and the early identification and prevention of its diseases are essential to ensure adequate and healthy availability to the world's growing population. The use of image processing is an encouraging method for automatic rice leaf disease identification and detection. In particular, the recent advancements indicate the effectiveness of convolutional neural network (CNN) based deep learning approaches. In this direction, the present work proposes a novel stacked parallel convolution layers‐based network (SPnet) with the squeeze‐and‐excitation (SE) architecture, named (SE_SPnet), for classifying diseased rice leaf images. The stacked parallel network block comprises four parallel convolution layers with different kernel sizes for abstractions of the global and local features. The SE block extracts feature information automatically while removing invalid ones. We compare the SE_SPnet model with state‐of‐the‐art CNN models such as VGG16, DenseNet121, and InceptionV3 based on computational effort, accuracy, sensitivity, specificity, precision, recall, and F1‐score. The experimental results show that the SE_SPnet outperforms standard CNN models for the considered rice leaf disease image datasets. In particular, the SE_SPnet achieves the highest accuracy (99.2%), sensitivity (98.2%), specificity (98.5%), precision (98.4%), recall (98.2%), and F1‐score (98.5%) while using stochastic gradient descent (with momentum) optimizer with a 0.01 learning rate. Furthermore, the SE_SPnet also exhibits to outperform when compared with some of the most recent and relevant existing works.

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“…Hassan et al [45] has also proposed a CNN architecture for plant disease diagnosis which uses depthwise separable convolution to improve the inception architecture. For diagnosing the nutritional deficiency of rice plants, Sharma et al [46] has combined such classifiers as InceptionResNetV2, Xception, DenseNet201, and VGG19 to extract different features and fuse them into the average strategy.…”

Section: Image Processing Based Rice Leaf Spots Identificationmentioning

confidence: 99%

“…In this paper, the performances of the CNN architectures with multi-feature fusion proposed by Hassan et al [45] and Sharma et al [46]…”

Section: Comparison With Existing Well-known Modelmentioning

confidence: 99%

Multi-Scale Features Fusion Convolutional Neural Networks for Rice Leaf Disease Identification

Wang¹,

Li²,

Chan³

et al. 2022

jist

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It is time-consuming and labor-intensive to detect rice diseases manually. The purpose of this research is to develop a convolutional neural networks (CNNs)-based system to automatically detect the diseased rice leaf infected with rice leaf blast, helminthosporium leaf blight, and bacterial leaf blight. The sizes of rice leaf spots vary with the severity of disease infection. A single model based CNN cannot effectively classify images, especially for images with objects of small size as well as multiple object scales, and complicated image background. In this research, a multiscale serial convolution neural network (MSSCNN) and a multiscale parallel convolution neural network (MSPCNN) are proposed to identify diseased rice leaf spots based on multi-modal fusion to extract different perception features and combine them to improve the performances obtained by using only one modality. Experimental results delineate that MSSCNN and MSPCNN can get better performance in identifying the diseased rice leaves. In MSPCNN, the features of tiny spots on diseased rice leaves can be completely preserved. MSPCNN can hence offer better performances than MSSCNN. Additionally, MSPCNN architecture is suitable for parallel computing environment.

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

Cited by 43 publications

References 42 publications

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

SE_SPnet: Rice leaf disease prediction using stacked parallel convolutional neural network with squeeze‐and‐excitation

Multi-Scale Features Fusion Convolutional Neural Networks for Rice Leaf Disease Identification

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