Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

Dhiman, Poonam; Kaur, Amandeep; Hamid, Yasir; Alabdulkreem, Eatedal; Elmannai, Hela; Ababneh, Nedal

doi:10.3390/su15054576

Cited by 25 publications

(9 citation statements)

References 21 publications

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“…It is worth noting that edge computing plays a pivotal role in enabling cloud computing and information technology services to be delivered at the network’s edge [ 47 ]. The open architecture of cloud computing and network access at the edge, on the other hand, exposes various attack routes [ 48 ]. Table 4 represents the comparative analysis of ZT techniques used by different platform.…”

Section: Comparative Analysis Of Zero Trust Network Systemmentioning

confidence: 99%

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model

Dhiman,

Saini,

Gulzar

et al. 2024

Sensors

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The Zero Trust safety architecture emerged as an intriguing approach for overcoming the shortcomings of standard network security solutions. This extensive survey study provides a meticulous explanation of the underlying principles of Zero Trust, as well as an assessment of the many strategies and possibilities for effective implementation. The survey begins by examining the role of authentication and access control within Zero Trust Architectures, and subsequently investigates innovative authentication, as well as access control solutions across different scenarios. It more deeply explores traditional techniques for encryption, micro-segmentation, and security automation, emphasizing their importance in achieving a secure Zero Trust environment. Zero Trust Architecture is explained in brief, along with the Taxonomy of Zero Trust Network Features. This review article provides useful insights into the Zero Trust paradigm, its approaches, problems, and future research objectives for scholars, practitioners, and policymakers. This survey contributes to the growth and implementation of secure network architectures in critical infrastructures by developing a deeper knowledge of Zero Trust.

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Section: Comparative Analysis Of Zero Trust Network Systemmentioning

confidence: 99%

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model

Dhiman,

Saini,

Gulzar

et al. 2024

Sensors

Self Cite

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“…Dhiman et al [11] introduced an effectual system to predict citrus fruit disease. This developed technique employs the combination of DL methods like CNN and LSTM with edge computing.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Assisted Plant Disease Detection and Crop Management using Deep Learning for Sustainable Agriculture

Al-Shahari,

Aldehim,

Aljebreen

et al. 2024

IEEE Access

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The integration of Internet of Things (IoT) technology with deep learning (DL) algorithms has revolutionized plant disease detection and crop management and paved the way for sustainable agricultural practices. Real-time information on soil moisture, plant health, and environmental conditions can be collected by deploying a network of connected devices and sensors in agricultural fields. DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. This early detection allows farmers to implement targeted interventions, like adjustment to irrigation or precision application of pesticides, maximizing crop yield, and minimizing resource wastage. Therefore, this article develops an automated Plant Disease Detection and Crop Management using a spotted hyena optimizer with deep learning (APDDCM-SHODL) technique for Sustainable Agriculture. The APDDCM-SHODL approach aims to detect the existence of plant diseases and improve crop productivity in the IoT infrastructure. To achieve this, the APDDCM-SHODL method primarily employs the Vector Median Filter (VMF) technique. In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. A brief set of experiments has been made to determine the experimental validation of the APDDCM-SHODL model. The comprehensive results inferred that the APDDCM-SHODL method reaches remarkable performance over other existing methods with the highest accuracy of 98.60%.

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“…This paper discusses concepts related to image acquisition processes, digital image processing, feature extraction, and classification approaches. Each concept is discussed separately [8]. It is crucial to employ image annotation techniques that are fast, simple, and highly effective.…”

Section: Related Studymentioning

confidence: 99%

Comparison of Deep Convolutional Neural Network Architectures for Fruit Categorization

Muliadi Kerta,

Haris Rangkuti,

Tantio

2024

Int. j. electr. comput. eng. syst. (Online)

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In general, there are so many types of fruit images that it is difficult for humans to differentiate them based on their visual characteristics alone. This research focuses on identifying and recognizing images of fruit from 23 different classes or types. Fruit varieties consist of 13 apple classes, 1 orange class, and 9 tomato classes, totaling 15,987 images. Fruit image data were collected from various sources, including the internet, magazines, and direct capture with a digital camera. The process of identifying and recognizing fruit images involves the classification of fruit images using a deep learning algorithm. Several CNN models, which are derivatives of deep learning, are used to achieve high accuracy and robustness in recognizing various types of apples and tomatoes. To evaluate the performance of each model, the apple data were trained on a large and diverse set of apple images using several CNN models such as ResNet50V2, InceptionV3, InceptionResNetV2, VGG16, VGG19, MobileNetV2, and EfficientNet. Performance is assessed using metrics such as accuracy, precision, recall, and F1 score. To achieve optimal performance in the image recognition process, it consists of preprocessing strategies, data augmentation, feature extraction, and classification supported by optimization, all of which have a significant impact on increasing accuracy performance. Experimental results show that certain CNN model architectures outperform other model architectures in terms of time efficiency and accuracy in recognizing fruit types/classes. However, to get more optimal results regarding the performance of the CNN model architecture for fruit categorization, two optimizers will be used, namely Adam and Adagrad, and will be compared. Based on Adam's optimizer experiments, the EfficientNet model produces the highest average accuracy of up to 99%, followed using the VGG 16 and ResNet V2 50 models, which achieve 98% and 97% accuracy. Meanwhile, the use of the Adagrad optimizer with the VGG 16 model produces the highest average accuracy of up to 95%, followed using the VGG 19 and EfficientNet models, which achieve accuracy of up to 93% and 91%. Overall, this experiment produced very good accuracy because it produced an average of above 90%. However, there is still room for improvement in recognizing fruits of different shapes, textures, and colors.

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Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

Cited by 25 publications

References 21 publications

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model

Internet of Things Assisted Plant Disease Detection and Crop Management using Deep Learning for Sustainable Agriculture

Comparison of Deep Convolutional Neural Network Architectures for Fruit Categorization

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