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 31 publications

(4 citation statements)

References 41 publications

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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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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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“…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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“…To improve the learning ability of the micro lesion characteristics, the network employs the Focal Loss function. M. Sharma et al [17] investigate six TL architectures, namely Xception, VGG16, InceptionV3, VGG19, DenseNet, InceptionResNetV2, and ResNet152V2, and their distinct ensemble models are applied to perform deficient diagnosis in rice crop to determine NPK nutrient deficiency and nitrogen deficiency.…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models

Sobhana

Vallabhaneni

Vasireddy

et al. 2022

Int. J. Interact. Mob. Technol.

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India is an agricultural country, and farming is the most common occupation among Indians. Rice is a vital crop in the agricultural industry. Productivity has been declining for almost a decade. There are several causes for this, including fragmented land holdings, Indian farmer illiteracy, a lack of decision-making capacity in selecting excellent seeds, manure, and irrigational infrastructure. One of the major reasons for rice crop failure is due to malnutrition. Rice, maybe in particular, lacking in nutrients such as potassium, nitrogen, and phosphorus. Nutrient deficiency detection in crops is necessary to plan further actions to enhance yield. Most studies have relied on the use of transfer learning models for agricultural uses. Ensembling of different transfer learning techniques has the ability to greatly increase the predictive model’s performance. Five transfer learning architectures InceptionV3, Xception, VGG16, Resnet50, and MobileNet are all taken into account, and their different ensemble models are used to perform deficiency detection in rice plants. The mobile application was created as a user-friendly interface to assist farmers. The accurate diagnosis of these nutritional deficiencies and recommendation of fertilizer could aid farmers in providing correct plant intervention.

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

Cited by 31 publications

References 41 publications

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

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

Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models

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