A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena, Sangeeta Vaibhav; Dhaka, Vijaypal Singh; Sinwar, Deepak; Kavita, .; Ijaz, Muhammad Fazal; Woźniak, Marcin

doi:10.3390/s21144749

Cited by 253 publications

(116 citation statements)

References 114 publications

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“…Convolutional neural networks are constructed by using several convolution layers which use learnable filters or kernels to identify patterns in images such as edges, texture, color, and shapes. CNN models possess several desirable properties that enable the extraction of complex features in images that would otherwise be hard to distill [ 26 ]. Since the success of AlexNet in the ImageNet large-scale image classification competition, several variants of CNNs have been invented that explore a variety of approaches to overcome the limitations of the standard CNN models [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Krishnamurthy¹,

Srinivasan

Qaisar

et al. 2021

Computational and Mathematical Methods in Medicine

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Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.

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Section: Methodsmentioning

confidence: 99%

Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Krishnamurthy¹,

Srinivasan

Qaisar

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Among pretrained models with more than a certain number of layers, a model with a large number of hyperparameters such as ResNet50 has good analysis performance. If precise tuning of hyperparameters was involved, the accuracy was relatively higher than that of the model with deeper layers (Dhaka et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…Thus, year after year, errors are reduced and accuracy is increased by changing the layer composition, depth, and calculation methods used in CNNs. In previous studies, several applications of CNN architectures showed outstanding results in the competition in the past decade (Dhaka et al, 2021 ) such as AlexNet (Krizhevsky et al, 2012 ), VGG19 (Simonyan and Zisserman, 2014 ), Inception v3 (Szegedy et al, 2016 ), Inception v4 (Szegedy et al, 2017 ), GoogLeNet (Szegedy et al, 2015 ), and ResNet50 (He et al, 2016 ), DenseNet121 (Huang et al, 2017 ), and SqueezeNet (Iandola et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Jung

Song²,

Hong

et al. 2021

Front. Plant Sci.

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Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures.

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“…Recently, the introduction of deep learning has led to significant advances in object recognition. There are many scholars who have applied deep learning methods in agriculture, including yield estimation by detecting fruits and improving crop quality by pest and disease detection (Gao et al, 2020;Mu et al, 2020;Dhaka et al, 2021;Kundu et al, 2021). Mu et al (2020) developed an R-CNN algorithm using Resnet-101 as the backbone network for the detection, counting, and size estimation of green tomatoes.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Fruit-Detection Algorithm for Edge Computing Applications

et al. 2021

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In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively low image processing capability of edge devices. Such limitations are becoming a new bottleneck and hindering the utilization of AI algorithms in modern horticulture. In this paper, we propose a lightweight fruit-detection algorithm, specifically designed for edge devices. The algorithm is based on Light-CSPNet as the backbone network, an improved feature-extraction module, a down-sampling method, and a feature-fusion module, and it ensures real-time detection on edge devices while maintaining the fruit-detection accuracy. The proposed algorithm was tested on three edge devices: NVIDIA Jetson Xavier NX, NVIDIA Jetson TX2, and NVIDIA Jetson NANO. The experimental results show that the average detection precision of the proposed algorithm for orange, tomato, and apple datasets are 0.93, 0.847, and 0.850, respectively. Deploying the algorithm, the detection speed of NVIDIA Jetson Xavier NX reaches 21.3, 24.8, and 22.2 FPS, while that of NVIDIA Jetson TX2 reaches 13.9, 14.1, and 14.5 FPS and that of NVIDIA Jetson NANO reaches 6.3, 5.0, and 8.5 FPS for the three datasets. Additionally, the proposed algorithm provides a component add/remove function to flexibly adjust the model structure, considering the trade-off between the detection accuracy and speed in practical usage.

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A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Cited by 253 publications

References 114 publications

Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Lightweight Fruit-Detection Algorithm for Edge Computing Applications

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