Journal ArticleDOI
A blockchain and deep neural networks-based secure framework for enhanced crop protection
Vikas Hassija,Siddharth Batra,Vinay Chamola,Tanmay Anand,Poonam Goyal,Navneet Goyal,Mohsen Guizani +6 more
- Vol. 119, pp 102537
TLDR
In this article, a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers' community is presented.Abstract:
The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate.read more
Citations
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Journal ArticleDOI
Technological revolutions in smart farming: Current trends, challenges & future directions
TL;DR: A detailed survey on ten hot-spots of smart farming is presented in this article , which covers the technology-wise state-of-the-art methods along with their application domains.
Journal ArticleDOI
Security in IoT-enabled smart agriculture: architecture, security solutions and challenges
Journal ArticleDOI
Concatenated deep features with modified LSTM for enhanced crop disease classification
Journal ArticleDOI
Automatic crop disease recognition by improved abnormality segmentation along with heuristic-based concatenated deep learning model
TL;DR: The main intention of this paper is to focus on the development of a novel crop disease detection model using the modified deep learning architecture, in which the Heuristic-based Convolutional Neural Network with Recurrent Neural Network (H-C-RNN) is developed.
Journal ArticleDOI
A crop yield prediction model based on an improved artificial neural network and yield monitoring using a blockchain technique
Manojkumar Sumathi,M. D. Rajkamal,S. Pravinth Raja,M. S. Venkatachalapathy,Nagarajan Vijayaraj +4 more
TL;DR: In this paper , a smart agriculture technique is proposed in which uses IoT sensor devices to collect data from farms, following which the fuzzy association rule-based classification technique classifies the data into two, valuable and nonvaluable.
References
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Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
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An overview of gradient descent optimization algorithms
TL;DR: This article looks at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.
Book ChapterDOI
Convolutional Neural Networks
TL;DR: Convolution Neural Networks (CNNs) in essence are neural networks that employ the convolution operation (instead of a fully connected layer) as one of its layers.
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weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
TL;DR: An approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV) using the recently developed encoder–decoder cascaded convolutional neural network, SegNet, that infers dense semantic classes while allowing any number of input image channels and class balancing with sugar beet and weed datasets.
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Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model
TL;DR: A novel privacy anonymous IoT model that leverages blockchain’s trust-oriented decentralization for on-chain data logging and retrieval that will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy is designed and presented.