scholarly journals Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

2020 ◽  
Vol 16 (2) ◽  
pp. 158-166
Author(s):  
Rishiikeshwer B. S. ◽  
T. Aswin Shriram ◽  
J. Sanjay Raju ◽  
M. Hari ◽  
B. Santhi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Mobile Application ◽  
Disease Detection ◽  
Data Set ◽  
Convolution Neural Networks ◽  
Leaf Disease

scholarly journals Real Time Object Detection Canbe Embedded on Low Powered Devices

2019 ◽  
Author(s):  
Jimut Bahan Pal
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Open Source ◽  
Real Time ◽  
Detection System ◽  
Computing Power ◽  
Convolution Neural Networks ◽  
Real Challenge ◽  
New Algorithms

It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time objectdetection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems.

Real-time multimodal ADL recognition using convolution neural networks

2020 ◽  
Author(s):  
Danushka Madhuranga ◽  
Rivindu Madushan ◽  
Chathuranga Siriwardane ◽  
Kutila Gunasekera
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolution Neural Networks

scholarly journals Neural network based approximation of muscle and joint contact forces during jumping and landing

2019 ◽  
Author(s):  
Daniel Cleather
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Contact Forces ◽  
Data Set ◽  
Video Record ◽  
Musculoskeletal Models ◽  
The Neural Network ◽  
Joint Contact ◽  
The Neural Networks

Musculoskeletal models have been used to estimate the muscle and joint contact forces expressed during movement. One limitation of this approach, however, is that such models are computationally demanding, which limits the possibility of using them for real-time feedback. One solution to this problem is to train a neural network to approximate the performance of the model, and then to use the neural network to give real-time feedback. In this study, neural networks were trained to approximate the FreeBody musculoskeletal model for jumping and landing tasks. The neural networks were better able to approximate jumping than landing, which was probably a result of the greater variability in the landing data set used in this study. In addition, a neural network that was based on a reduced set of inputs was also trained to approximate the outputs of FreeBody during a landing task. These results demonstrate the feasibility of using neural networks to approximate the results of musculoskeletal models in order to provide real-time feedback. In addition, these neural networks could be based upon a reduced set of kinematic variables taken from a 2-dimensional video record, making the implementation of mobile applications a possibility.

scholarly journals Fusing Convolutional Neural Networks to Improve the Accuracy of Plant Leaf Disease Classification

2020 ◽  
pp. 9-19
Author(s):  
Bhavana Nerkar ◽  
Sanjay Talbar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Highly Effective ◽  
Effective Network ◽  
Number Of Classes

Aims: This text aims to improve the accuracy of plant leaf disease detection using a fused convolutional neural network architecture Study Design:  In this study, propose a hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and reduce the delay needed for leaf disease classification. Place and Duration of Study: National institute of electronics and information technology Aurangabad, between June 2018 and September 2020. Methodology: Convolutional neural networks (CNNs) have become a de-facto technique for classification of multi-dimensional data. Activation functions like rectified linear unit (ReLU), softmax, sigmoid, etc. have proven to be highly effective when doing so. Moreover, standard CNN architectures like AlexNet, VGGNet, Google net, etc. further assist this process by providing standard and highly effective network layer arrangements. But these architectures are limited by the speed due to high number of calculations needed to train and test the network. Moreover, as the number of classes increase, there is a reduction in validation and testing accuracy for the networks. In order to remove these drawbacks, hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and speed of leaf classification. Results: The developed system was tested on different kinds of leaf diseases, and it was observed that the proposed system obtains more than 98% accuracy for both testing and validation sets. Conclusion: It is observed that the delay is reduced, while the accuracy is improved by the most effective classifiers. This encourage us to use the proposed system for real-time leaf image disease detection.

scholarly journals Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

2020 ◽  
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Fruits And Vegetables ◽  
Early Stage ◽  
Economic Loss ◽  
Disease Diagnosis ◽  
Data Set ◽  
Plant Leaf ◽  
Leaf Disease

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.

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