Image Driven Multi Feature Plant Management with FDE Based Smart Agriculture with Improved Security in Wireless Sensor Networks

Jayagopalan, Santhosh; Balamurugan, P.; Arulkumaran, G.; Baskar, M.; Velumani, R.

doi:10.1007/s11277-021-08710-x

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…However, they usually experience devices with "regular" speeds because there are often many factors that affect bandwidth. Conversely, it is important to look at realistic speeds or the average measured bandwidth [24][25][26][27][28]. The 5G has not been released yet, so we cannot comment on the real-world experience, but at least 5 GB.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Spectrum Sharing and Allocation for Multiuser-Based 5G Cellular Networks

Vasanth

Yuvaraj

Janga³

et al. 2022

Wireless Communications and Mobile Computing

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The number of users of current mobile and cellular networks is constantly increasing. The allocation of spectrum to its users is constantly facing various hurdles. This causes users to leave that network and connect to another network. Thus, those telecom companies are constantly struggling to provide high-speed services to the users. As a result, the demand for 5G networks is currently increasing. Considering these, an algorithm has been proposed here to suit the needs of the users. Its main feature is that it easily identifies the primary and secondary users of the 5G network and creates a spectrum allocation system for them accordingly. Generally, all other methods are designed with the primary user in mind. Furthermore, the spectrum hole calculation that is currently being proposed is done accurately so that the spectrum switching processes required for the secondary user can take place here very quickly so that the secondary user can use the spectrum without any hindrance. The proposed model achieved 93.29% of spectrum blocking, 6.71% of spectrum band dropping, 94.03% bandwidth utilization, 1073 ms end-to-end delay, and 17273 bps of throughput. The proposed model effectively handles the spectrum and intelligent approach to resolve the spectrum hole problems. The existing models are practically faced with these problems. The proposed model spectrum utilization and efficiency were increased compared with the existing models.

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Section: Introductionmentioning

confidence: 99%

Context-Aware Spectrum Sharing and Allocation for Multiuser-Based 5G Cellular Networks

Vasanth

Yuvaraj

Janga³

et al. 2022

Wireless Communications and Mobile Computing

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“…A large volume of data is required to train the DCNN models for use in various domains [ 8 ]. The data augmentation technique was introduced to increase the amount of training data without data collection for better training performance of DCNN models [ 9 ]. Training the DCNN model needs huge computation and storage.The graphics processing units (GPUs) are commonly used to train models more efficiently [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

Pandian

Kanchanadevi

Rajalakshmi

et al. 2022

Computational Intelligence and Neuroscience

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In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healthy and diseased plant leaves. The evolutionary search technique was used to optimise the layers and hyperparameter values of ResNet197. ResNet197 was trained on the combined plant leaf disease image dataset using a graphics processing unit (GPU) environment for 1000 epochs. It produced a 99.58 percentage average classification accuracy on the test dataset. The experimental results were superior to existing ResNet architectures and recent transfer learning techniques.

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“…In order to protect the security of agricultural information, it is necessary to build a scheme suitable for agricultural image encryption. An image-driven multi-feature plant management model based on feature data encryption scheme is constructed by Santhosh et al [19], which used dynamic scheme and key to encrypt data, improving the security and performance of smart agriculture. Perumal et al [20] realized data security of different smart devices in farmland by using data encryption schemes, which used different encryption schemes and keys to encrypt data of farmland devices controlled by users, thus achieving higher accuracy of low-rate attack detection.…”

mentioning

confidence: 99%

Research on Wheat Image Encryption Based on Different Chaotic Systems

Shao,

Zhu,

Deng

et al. 2024

Preprint

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(1) In order to study the influence of chaotic system on wheat image encryption, a method to find the optimal chaotic system encryption based on wheat image is proposed. (2) 10 different chaotic system schemes are combined to encrypt wheat images by 13 common chaotic maps, respectively. The best chaotic system scheme is obtained by considering the anti-attack capability of these encryption schemes, which analyzes 8 commonly used image encryption performance evaluation indexes. (3) The experimental results show that the new four-dimensional chaotic system has the best encryption effect, which is suitable for wheat image encryption. (4) The proposed scheme of wheat image encryption based on chaotic system provides a reference for other crop image encryption.

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Image Driven Multi Feature Plant Management with FDE Based Smart Agriculture with Improved Security in Wireless Sensor Networks

Cited by 7 publications

References 22 publications

Context-Aware Spectrum Sharing and Allocation for Multiuser-Based 5G Cellular Networks

Context-Aware Spectrum Sharing and Allocation for Multiuser-Based 5G Cellular Networks

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

Research on Wheat Image Encryption Based on Different Chaotic Systems

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