scholarly journals A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Aitor Gutierrez ◽  
Ander Ansuategi ◽  
Loreto Susperregi ◽  
Carlos Tubío ◽  
Ivan Rankić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Learning Strategies ◽  
Bemisia Tabaci ◽  
Trialeurodes Vaporariorum ◽  
Identification Accuracy ◽  
Tomato Plants ◽  
Detection And Identification ◽  
Pest Detection

Greenhouse crop production is growing throughout the world and early pest detection is of particular importance in terms of productivity and reduction of the use of pesticides. Conventional eye observation methods are nonefficient for large crops. Computer vision and recent advances in deep learning can play an important role in increasing the reliability and productivity. This paper presents the development and comparison of two different approaches for vision based automated pest detection and identification, using learning strategies. A solution that combines computer vision and machine learning is compared against a deep learning solution. The main focus of our work is on the selection of the best approach based on pest detection and identification accuracy. The inspection is focused on the most harmful pests on greenhouse tomato and pepper crops, Bemisia tabaci and Trialeurodes vaporariorum. A dataset with a huge number of infected tomato plants images was created to generate and evaluate machine learning and deep learning models. The results showed that the deep learning technique provides a better solution because (a) it achieves the disease detection and classification in one step, (b) gets better accuracy, (c) can distinguish better between Bemisia tabaci and Trialeurodes vaporariorum, and (d) allows balancing between speed and accuracy by choosing different models.

scholarly journals Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals Tomato torrado virus is Transmitted by Bemisia tabaci and Infects Pepper and Eggplant in Addition to Tomato

Plant Disease ◽  
2008 ◽  
Vol 92 (7) ◽  
pp. 1139-1139 ◽  
Author(s):  
K. Amari ◽  
D. Gonzalez-Ibeas ◽  
P. Gómez ◽  
R. N. Sempere ◽  
M. A. Sanchez-Pina ◽  
...  
Keyword(s):  
Host Range ◽  
Bemisia Tabaci ◽  
Chenopodium Quinoa ◽  
Trialeurodes Vaporariorum ◽  
Virus Genome ◽  
Molecular Hybridization ◽  
Cdna Libraries ◽  
Economic Losses ◽  
Tomato Plants ◽  
Tomato Seedlings

Torrao or torrado is an emerging disease that is causing serious economic losses in tomato crops of southeastern Spain. The causal agent has been shown to be a new picorna-like plant virus, tentatively named Tomato torrado virus (ToTV) (4). By using trap tomato plants in a greenhouse affected by torrado located in the Murcia Region of Spain, we obtained a ToTV isolate (ToTV-CE) that we have biologically and molecularly characterized. Subtracted cDNA libraries (1) and expressed sequence tags sequencing were used to determine the partial nucleotide sequence of ToTV-CE. We covered ≈53% of the virus genome (GenBank Accession Nos. EU476181 and EU476182) and found that ToTV-CE RNAs 1 and 2 had a high nucleotide similarity (98 and 99%, respectively) with the ToTV published sequences (2,4). ToTV-CE sequences also showed a 70% nt similarity with those of Tomato apex necrosis virus, a newly identified virus in tomato crops of the Culiacan area (Sinaloa, Mexico) (3). To characterize the host range of ToTV-CE, 6 to 10 plants belonging to 14 species were mechanically inoculated with extracts from ToTV-CE-infected Nicotiana benthamiana plants. The presence of ToTV in these plants was analyzed at 3 and 6 weeks postinoculation (PI) by molecular hybridization in dot-blots. The determined host range was in agreement with that described earlier (2,4), but additional hosts and nonhosts were identified. Thus, the virus did not infect melon (Cucumis melo var. cantaloupe), cucumber (C. sativus cv. Marketmore), squash (Cucurbita pepo cv. Negro Belleza), Chenopodium album ssp. Amaranticolor, or Chenopodium quinoa. The virus infected systemically N. benthamiana, N. glutinosa, N. rustica, tobacco (N. tabacum cvs. Xanthi nc and Samsun), Physalis floridana, pepper (Capsicum annuum cv. Italian Long Sweet), tomato (Solanum lycopersicum cv. Boludo), and eggplant (S. melongena cv. Black Beauty). Pepper plants displayed severe symptoms of infection consisting of marked mosaics and stunting (but no necrosis), but eggplant remained asymptomatic for up to 6 weeks PI. A simple assay was devised to analyze whether ToTV can be transmitted by whiteflies. ToTV-CE-infected tomato plants were placed together with three to eight healthy tomato seedlings inside insect-proof glass boxes. Adult Bemisia tabaci (100 to 800 individuals in three replicates) or Trialeurodes vaporariorum (100 individuals in one replicate) were released into each box. For both treatments, symptoms typically induced by ToTV appeared in one to seven tomato seedlings by 1 week after the release of the whiteflies. ToTV infection was confirmed by molecular hybridization in tissue prints of petiole cross sections at 10 days PI. These data are in agreement with those reported by Pospieszny et al. (2) and strongly suggest that both B. tabaci and T. vaporariorum can transmit ToTV. References: (1) L. Diachenko et al. Proc. Natl. Acad. Sci. USA 93:6025, 1996. (2) H. Pospieszny et al. Plant Dis. 91:1364, 2007 (3) M. Turina et al. Plant Dis. 91:932, 2007. (4) M. Verbeek et al. Arch. Virol. 152:881, 2007.

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

scholarly journals Penerapan Computer Vision Menggunakan Metode Deep Learning pada Perspektif Generasi Ulul Albab

2021 ◽  
Vol 7 (2) ◽  
pp. 98-107
Author(s):  
Imamul Arifin ◽  
Reydiko Fakhran Haidi ◽  
Muhammad Dzalhaqi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Library Research

Machine learning merupakan salah satu penerapan kecerdasan buatan. Penggunaan machine learning pada computer vision erat berkaitan dengan deep learning yang mana para ilmuwan komputer mendapatkan inspirasi mengenai teknologi deep learning dari alam sekitar. Tujuan penelitian pada naskah ini adalah Mengetahui dan memahami teknologi deep learning beserta contoh sederhana dalam pemrosesan objek gambar dan Mengetahui dan memahami teknologi kecerdasan buatan dalam perspektif generasi ulul albab sehingga bisa memberikan manfaat secara menyeluruh bagi dunia. Penelitian yang dilakukan pada karya tulis ini merupakan jenis penelitian kualitatif dengan metode studi pustaka (library research) menggunakan berbagai buku dan literatur bacaan lainnya seperti jurnal dan website khusus sehingga menghasilkan informasi dari topik yang diteliti. Teknologi kecerdasan buatan akan selalu berkembang dan menuju arah yang semakin canggih, tetapi teknologi juga mempunyai dampak negatif. Generasi Ulul Albab harus bisa berjuang untuk memberikan dampak positif bagi masyarakat karena sejatinya generasi ulul albab adalah harapan kemajuan peradaban islam di berbagai sektor ilmu pengetahuan dan teknologi.

scholarly journals Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 279 ◽  
Author(s):  
Bambang Susilo ◽  
Riri Fitri Sari
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Strategies ◽  
Learning Algorithm ◽  
Human Life ◽  
Denial Of Service ◽  
The Internet ◽  
Dos Attacks ◽  
Deep Learning Algorithm ◽  
Iot Devices

The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

scholarly journals Computational Analysis of Deep Visual Data for Quantifying Facial Expression Production

2019 ◽  
Vol 9 (21) ◽  
pp. 4542 ◽  
Author(s):  
Marco Leo ◽  
Pierluigi Carcagnì ◽  
Cosimo Distante ◽  
Pier Luigi Mazzeo ◽  
Paolo Spagnolo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Facial Expression ◽  
Learning Strategies ◽  
Facial Expressions ◽  
Computational Analysis ◽  
Human Perception ◽  
Autism Spectrum ◽  
Virtual Sensors ◽  
The Individual

The computational analysis of facial expressions is an emerging research topic that could overcome the limitations of human perception and get quick and objective outcomes in the assessment of neurodevelopmental disorders (e.g., Autism Spectrum Disorders, ASD). Unfortunately, there have been only a few attempts to quantify facial expression production and most of the scientific literature aims at the easier task of recognizing if either a facial expression is present or not. Some attempts to face this challenging task exist but they do not provide a comprehensive study based on the comparison between human and automatic outcomes in quantifying children’s ability to produce basic emotions. Furthermore, these works do not exploit the latest solutions in computer vision and machine learning. Finally, they generally focus only on a homogeneous (in terms of cognitive capabilities) group of individuals. To fill this gap, in this paper some advanced computer vision and machine learning strategies are integrated into a framework aimed to computationally analyze how both ASD and typically developing children produce facial expressions. The framework locates and tracks a number of landmarks (virtual electromyography sensors) with the aim of monitoring facial muscle movements involved in facial expression production. The output of these virtual sensors is then fused to model the individual ability to produce facial expressions. Gathered computational outcomes have been correlated with the evaluation provided by psychologists and evidence has been given that shows how the proposed framework could be effectively exploited to deeply analyze the emotional competence of ASD children to produce facial expressions.

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