Application of Pre-Trained Deep Convolutional Neural Networks for Rice Plant Disease Classification

Shrivastava, Vimal K.; Pradhan, Monoj K.; Thakur, Mayur

doi:10.1109/icais50930.2021.9395813

Cited by 41 publications

(10 citation statements)

References 25 publications

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“…Notably, this model did not draw upon pre-trained weights as an objective of the study was to evaluate the robustness of image-based learning with in-house data taken directly from temporal drone images. However, pre-trained networks have demonstrated success in tasks such as classifying tomato diseases (40), rice diseases (41), and identifying plant pests (42), though largely in controlled environments where SPA is routine. After developing a CNN aimed at identifying species of Miridae ("plant bugs") using curated training images, Knyshov et al (42) reported a model accuracy of 62% when applied to live field images, indicating generalizability of the model despite a modest number of images belonging to each class.…”

Section: Discussionmentioning

confidence: 99%

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

DeSalvio,

Arik,

Murray

et al. 2024

Preprint

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Senescence is a degenerative biological process that affects most organisms. Timing of senescence is critical for annual and perennial crops and is associated with yield and quality. Tracking time-series senescence data has previously required expert annotation and can be laborious for large-scale research. Here, a convolutional neural network (CNN) was trained on unoccupied aerial system (UAS, drone) images of individual plants of cotton (Gossypium hirsutum L.), an early application of single-plant analysis (SPA). Using images from 14 UAS flights capturing most of the senescence window, the CNN achieved 71.4% overall classification accuracy across six senescence categories, with class accuracies ranging between 46.8–89.4% despite large imbalances in numbers of images across classes. For example, the number of images ranged from 109 to 1,129 for the lowest-performing class (80% senesced) to the highest-performing class (fully healthy). The results demonstrate that minimally pre-processed UAS images can enable translatable implementations of high-throughput phenotyping using deep learning methods. This has applications for understanding fundamental plant biology, monitoring orchards and other spaced plantings, plant breeding, and genetic research.

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Section: Discussionmentioning

confidence: 99%

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

DeSalvio,

Arik,

Murray

et al. 2024

Preprint

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“…A significant development of deep learning has given a new dimension in machine learning domain during the last decades. The convolutional neural network (CNN) has emerged as most powerful model among various deep learning models for visual recognition tasks [17][18][19]. Figure 1 CNN's main component is the convolution layer.…”

Section: Methodsmentioning

confidence: 99%

Diagnosis of COVID-19 based on chest X-ray images using pre-trained deep convolutional neural networks

Shrivastava

Pradhan

2022

IDT

Self Cite

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The novel coronavirus (COVID-19) that emerged and transmitted from China (Wuhan City) had a staggering effect on public health and the world economy. The early diagnosis of COVID-19 has become more important for its treatment and for controlling its spread due to its highly transmissible nature. In addition, the restricted supply of test kits calls for an alternative system for diagnosis. Since radiological images of chest of patients with COVID-19 show abnormalities, it is possible to diagnose COVID-19 utilizing chest X-ray images. Therefore, by applying deep convolution neural network (CNN), we have presented a diagnosis of COVID-19 based on chest X-ray images in this paper. For the diagnosis of COVID-19, an exhaustive comparative performance analysis of 16 state-of-the-art models is presented. Moreover, each model is trained with three approaches: transfer learning, fine tuning and scratch learning. The experiments were conducted on the dataset that comprises of 127 images of COVID-19, 500 images of Pneumonia and 500 images of normal cases. We have performed the experiments in two scenarios: binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Pneumonia vs. Normal). Further, we have applied cost-sensitive learning technique to handle the class imbalance issue. In this study, InceptionResNetV2 model with fine-tuning approach achieved highest classification accuracy of 99.20% in binary classification and Xception model achieved classification accuracy of 89.33% in multiclass classification among all considered models. To validate our approach, we have presented the performance of our model on three other datasets and achieved adequate classification accuracy. Hence, the promising results demonstrate that the fine-tuning of deep CNN models is an effective way for diagnosis of COVID-19 and therefore, it can be deployed in diagnostic centers to assist radiologist after its validation with more prominent datasets.

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“…Many previous studies have shown that the NASNeTMobile model performs well, such as the classification of rice diseases with an accuracy of 85.9% [29], ECG signal classification for cardiac examination [30] with an accuracy of 97.1 %, lung nodule classification from CT lung images with an accuracy of 88.28% [31] and skin lesion classification from dermoscopic images with an accuracy of 88.28% [32]. For on-device and embedded applications, the proposed MobileNetV2 has a low-latency, low-computation architecture.…”

Section: ) Data Acquisitionmentioning

confidence: 99%

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Razali¹,

Tony²,

Ibrahim³

et al. 2023

IJACSA

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Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challenging due to the common structure and the complexity of scene objects such as building, monuments and parks. Hence, this study proposes a super lightweight and robust landmark recognition model by using the combination of Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) approaches. The landmark recognition model was evaluated by using several pretrained CNN architectures for feature extraction. Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. The evaluations were performed on UMS landmark dataset and Scene-15 dataset. The results from the experiments have found that the Efficient Net (EFFNET) with CNN classifier are the best feature extraction and classifier. EFFNET-CNN achieved 100% and 94.26% classification accuracy on UMS-Scene and Scene-15 dataset respectively. Moreover, the feature dimensions created by EFFNet are more compact compared to the other features and even have significantly reduced for more than 90% by using Linear Discriminant Analysis (LDA) without jeopardizing classification performance but yet improved its performance.

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Application of Pre-Trained Deep Convolutional Neural Networks for Rice Plant Disease Classification

Cited by 41 publications

References 25 publications

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images

Diagnosis of COVID-19 based on chest X-ray images using pre-trained deep convolutional neural networks

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

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