An Image Dataset Construction for Flower Recognition Using Convolutional Neural Network

Omer, Saman M.; Hasan, Rayan M.; Anwer, Banaz N.

doi:10.25271/sjuoz.2020.8.3.747

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Table 1 shows the recognition rate of a certain movement and the average recognition rate of all movements in the patient posture behavior recognition system. e characteristic of convolutional neural network is that convolutional neural network introduces the concepts of weight sharing (convolutional kernel) and receptive field, which greatly reduces the number of parameters that the network needs to learn; the convolutional neural network can reduce the amount of calculation and save the time of calculation and has stronger learning ability and computing power [19]. Improve the efficiency of emergency nursing and rescue.…”

Section: Results and Analysismentioning

confidence: 99%

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

Chen

Zhong

2021

Journal of Healthcare Engineering

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In order to help pathologists quickly locate the lesion area, improve the diagnostic efficiency, and reduce missed diagnosis, a convolutional neural network algorithm for the optimization of emergency nursing rescue efficiency of critical patients was proposed. Specifically, three convolution layers and convolution kernels of different sizes are used to extract the features of patients’ posture behavior, and the classifier of patients’ posture behavior recognition system is used to learn the feature information by capturing the nonlinear relationship between the features to achieve accurate classification. By testing the accuracy of patient posture behavior feature extraction, the recognition rate of a certain action, and the average recognition rate of all actions in the patient body behavior recognition system, it is proved that the convolution neural network algorithm can greatly improve the efficiency of emergency nursing. The algorithm is applied to the patient posture behavior detection system, so as to realize the identification and monitoring of patients and improve the level of intelligent medical care. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient posture behavior feature extraction is, and the average recognition rate of patient posture behavior category is 97.6%, thus verifying the effectiveness and correctness of the system, to prove that the convolutional neural network algorithm has a very large improvement of emergency nursing rescue efficiency.

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Section: Results and Analysismentioning

confidence: 99%

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

Chen

Zhong

2021

Journal of Healthcare Engineering

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“…Despite reducing the complicated handcraft engineering processes, CNN is used to obtain a good performance of classifcation accuracy [10]. CNN has a good performance in terms of cucumber disease diagnosis, general image categorization, and leaf classifcation [11], as well as in fower recognition [12]. In [9], we realized that deep CNN in plant disease recognition is remarkably higher than the traditional algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

Omer

Ghafoor

Askar

2022

Mobile Information Systems

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In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms.

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Flower perception using Convolution Neural Networks based Escalation of Transfer learning

Murugeswari

Nila

Dhananjeyan

et al. 2022

2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT)

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An Image Dataset Construction for Flower Recognition Using Convolutional Neural Network

Cited by 3 publications

References 11 publications

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

Flower perception using Convolution Neural Networks based Escalation of Transfer learning

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