Role of Machine Learning and Cloud-Driven Platform in IoT-Based Smart Farming

Pradhan, Astik Kumar; Swain, Satyajit; Rout, Jitendra Kumar

doi:10.1007/978-981-16-5090-1_4

Cited by 10 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, image processing techniques with deep neural networks have been effectively used diversely and proven to be highly effective approaches in constantly monitoring the health of plants and identifying signs of diseases in their early stages [5]. A neural network considers an image of a diseased plant as an input and processes the image to produce a cropdisease pair.…”

Section: Introductionmentioning

confidence: 99%

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

Tabbakh

Barpanda

2023

IEEE Access

View full text Add to dashboard Cite

This paper proposes a novel approach for extracting deep features and classifying diseased plant leaves. The agriculture industry is negatively impacted by plant diseases causing crop and economic loss. Accurate and timely diagnosis is crucial for managing and controlling plant diseases, as traditional methods can be costly and time-consuming. Deep learning-based tools effectively detect plant diseases depending on the qualitative of extracted features. In this regard, a hybrid model for plant disease classification based on a Transfer Learning-based model followed by a vision transformer (TLMViT) is proposed. TLMViT has four stages: 1) data acquisition, where the PlantVillage and wheat datasets are used to train and evaluate the proposed model, 2) image augmentation to increase the number of training samples and overcome the overfitting issue, 3) leaf feature extraction by two consecutive phases: initial features extraction by using pre-trained based model and deep features extraction by using ViT model, and 4) classification by using MLP classifier. TLMViT is experimented with using five pre-trained-based models followed by ViT individually. TLMViT performs accurately in plant disease classification, obtaining 98.81% and 99.86% validation accuracy for VGG19 followed by the ViT model on PlantVillage and wheat datasets respectively. Moreover, TLMViT is compared with pre-trained-based architecture. The comparison result illustrates that TLMViT achieved an enhancement of 1.11% and 1.099% in validation accuracy, 2.576% and 2.92% in validation loss compared with the transfer learning-based model for PlantVillage and wheat datasets respectively. Thereby proposed model proves the efficiency of using ViT for extracting deep features from the leaf.INDEX TERMS Plant disease, image processing, deep learning, transfer learning, vision transformer.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

Tabbakh

Barpanda

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The development of deep learning has brought some new minds to cryptanalysis. In recent years, deep learning has spread across almost every field of science and technology (medical [12], agriculture [13], etc.) and has made remarkable progress on many difficult tasks.…”

Section: Introductionmentioning

confidence: 99%

Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck

2023

Applied Sciences

View full text Add to dashboard Cite

At CRYPTO 2019, Gohr proposed the neural differential distinguisher using the residual network structure in convolutional neural networks on round-reduced Speck32/64. In this paper, we construct a 7-round differential neural distinguisher for Speck32/64, which results in better than Gohr’s work. The details are as follows. Firstly, a new data format (C_r,C_r′,d_l,Cl,Cr,Cl′,Cr′) is proposed for the input data of the differential neural distinguisher, which can help the distinguisher to identify the features of the previous round of ciphertexts in the Speck algorithm. Secondly, this paper modifies the convolution layer of the residual block in the residual network, inspired by the Inception module in GoogLeNet. For Speck32/64, the experiments show that the accuracy of the 7-round differential neural distinguisher is 97.13%, which is better than the accuracy of Gohr’s distinguisher of 9.1% and also higher than the currently known accuracy of 89.63%. The experiments also show that the data format and neural network in this paper can improve the accuracy of the distinguisher by 2.38% and 2.1%, respectively. Finally, to demonstrate the effectiveness of the distinguisher in this paper, a key recovery attack is performed on 8-rounds of Speck32/64. The results show that the success rate of recovering the correct key is 92%, with no more than two incorrect bits. Finally, this paper briefly discussed the effect of the number of ciphertext pairs in a sample on the training results of the differential neural distinguisher. When the total number of ciphertext pairs is kept constant, the accuracy of the distinguisher increases with s, but it also leads to the occurrence of overfitting.

show abstract

“…Figure 2 encapsulates the research concept of utilizing the UTAUT model to refine the strategic approach to intelligent hog farming. It focuses on three core constructs: performance expectations, effort expectations, and social influence [38][39][40]. Performance expectations Our research specifically aims to provide actionable policy recommendations to foster sustainable development and technological upgrades.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2 encapsulates the research concept of utilizing the UTAUT model to refine the strategic approach to intelligent hog farming. It focuses on three core constructs: performance expectations, effort expectations, and social influence [38][39][40]. Performance expectations guide enterprises toward achieving operational efficiency and quality and are synergistic with industry trends toward scaling and competitiveness [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hog Farming Adoption Choices Using the Unified Theory of Acceptance and Use of Technology Model: Perspectives from China’s New Agricultural Managers

Wang,

Zhang,

Zhang

2023

Agriculture

View full text Add to dashboard Cite

This research delves into the intricacies of decision-making processes underpinning the willingness to upgrade technology within the burgeoning domain of intelligent pig farming in China, employing the UTAUT model to scrutinize how various determinants sway upgrade willingness and the ensuing behavioral modification. By applying the UTAUT model to intelligent pig farming, the inquiry evaluates the impact of performance expectations, effort expectations, social influence, and contributory factors on upgrade willingness and behavior, with data amassed from assorted novel agricultural management entities in China. The findings unveil that performance and effort expectations, social influence, and contributory factors have a favorable influence on upgrade willingness, while contributory factors, alongside the augmentation of upgrade willingness, positively affect upgraded behavior. This inquiry underscores the multifaceted interaction of factors guiding technological upgrade verdicts in intelligent pig farming, furnishing invaluable insights for comprehending technology adoption in agriculture. It lays a groundwork for devising strategies to spur technological advancements, harboring potential for wider applications across varied agricultural vistas.

show abstract

Role of Machine Learning and Cloud-Driven Platform in IoT-Based Smart Farming

Cited by 10 publications

References 16 publications

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck

Intelligent Hog Farming Adoption Choices Using the Unified Theory of Acceptance and Use of Technology Model: Perspectives from China’s New Agricultural Managers

Contact Info

Product

Resources

About