scholarly journals Irrigation System Using Hyperspectral Data and Machnie Learning Techniques for Smart Agriculture

2020 ◽  
Vol 16 (4) ◽  
pp. 576-582
Author(s):  
Santhi Balachandran ◽  
Sourna Lakshmi ◽  
Nithya Rajendran
Keyword(s):  
Irrigation System ◽  
Hyperspectral Data ◽  
Learning Techniques ◽  
Smart Agriculture

scholarly journals Practical Design of a WSN to Monitor the Crop and its Irrigation System

2019 ◽  
Vol 10 (4) ◽  
pp. 35 ◽  
Author(s):  
Laura García ◽  
Lorena Parra ◽  
Jose M. Jimenez ◽  
Jaime Lloret ◽  
Pascal Lorenz
Keyword(s):  
Precision Agriculture ◽  
Wireless Mesh Network ◽  
Irrigation System ◽  
Mesh Network ◽  
Sensors And Actuators ◽  
Wireless Network Design ◽  
Wireless Mesh ◽  
Practical Design ◽  
Smart Agriculture ◽  
Iot Devices

Due to environmental problems, such as the lack of water for irrigation, each day it becomes more necessary to control crops. Therefore, the use of precision agriculture becomes more evident. When it comes to making decisions on crops, it is evident the need to apply the concept of Smart Agriculture, that focuses on utilizing different sensors and actuators. As the number of IoT devices used in agriculture grows exponentially, it is necessary to design the implemented network so that the data is transmitted without problems. The present work shows a wireless network design, in which we use the information collected by the sensors of a Wireless Sensor Network (WSN), and a Wireless Mesh Network (WMN) formed by Access Points (AP) to transmit the data to a network that monitors agriculture for smart irrigation. In addition, through simulations we have presented a proposal of the maximum number of nodes that must be connected to an AP so that the network is efficient.

scholarly journals Hyperspectral Image Classification Using Parallel Autoencoding Diabolo Networks on Multi-Core and Many-Core Architectures

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 411 ◽  
Author(s):  
Emanuele Torti ◽  
Alessandro Fontanella ◽  
Antonio Plaza ◽  
Javier Plaza ◽  
Francesco Leporati
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Machine Learning Techniques ◽  
High Data ◽  
Learning Techniques ◽  
Speed Up ◽  
Many Core

One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain.

A supervised technique for drill-core mineral mapping using Hyperspectral data

2020 ◽  
Author(s):  
Cecilia Contreras ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Core Logging ◽  
Learning Techniques

<p>Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores<br>are first logged in the field by a geologist and with regards to, e.g., mineral assemblages,<br>alteration patterns, and structural features. The core-logging information is then used to<br>locate and target the important ore accumulations and select representative samples that are<br>further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray<br>diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and<br>subject to the expertise of the geologist.<br>Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly<br>being used to support the geologist in the analysis of drill-core samples. Nonetheless, the<br>benefit and impact of using hyperspectral data depend on the applied methods. With this in<br>mind, machine learning techniques, which have been applied in different research fields,<br>provide useful tools for an advance and more automatic analysis of the data. Lately, machine<br>learning frameworks are also being implemented for mapping minerals in drill-core<br>hyperspectral data.<br>In this context, this work follows an approach to map minerals on drill-core hyperspectral data<br>using supervised machine learning techniques, in which SEM data, integrated with the mineral<br>liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution<br>mineralogical data obtained by SEM-MLA analysis is resampled and co-registered<br>to the hyperspectral data to generate a training set. Due to the large difference in spatial<br>resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is<br>required to link these two images at the hyperspectral data spatial resolution. In this study,<br>we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral<br>pixel in the corresponding SEM-MLA region. We then use the abundances as features in a<br>clustering procedure to generate the training labels. In the final step, the generated training<br>set is fed into a supervised classification technique for the mineral mapping over a large area<br>of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave<br>infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral<br>mapping task improves significantly.</p>

scholarly journals Modeling of an irrigation system in a virtual physical space

2021 ◽  
Vol 18 (5) ◽  
pp. 6841-6856
Author(s):  
Todorka Glushkova ◽  
◽  
Stanimir Stoyanov ◽  
Lyubka Doukovska ◽  
Jordan Todorov ◽  
...  
Keyword(s):  
Intelligent Agents ◽  
Irrigation System ◽  
Physical Space ◽  
Layered System ◽  
Vegetable Production ◽  
Active Components ◽  
Crop Cycle ◽  
Personal Assistants ◽  
Smart Agriculture ◽  
Intelligent Irrigation

<abstract> <p>One of the major challenges that smart agriculture is expected to address is the efficient use of water resources. The conservation and the efficient use of clean water is a long-term strategy worldwide. Modeling of smart agriculture systems is an important factor because the processes there are very slow and sometimes it takes a year or more for a full crop cycle. At the same time, a large amount of data is usually needed to make informed decisions. This determines the importance of developing appropriate systems through which to simulate, generate, optimize and analyze various possible scenarios and prepare appropriate plans. In this paper, an infrastructure known as Virtual-Physical Space adapted for agriculture is presented. The space supports integration of the virtual and physical worlds where analysis and decision making are done in the virtual environment and the state of the physical objects (things) of interest is also taken into account at the same time. Special attention is paid to the possibilities for modeling an irrigation system. An ambient-oriented approach has been adopted, using the Calculus of Context-aware Ambients formalism as the basic tool for modeling agriculture processes. Furthermore, the supporting platform is briefly presented. Active components of the platform are implemented as intelligent agents known as assistants. Users (agriculture operators) are serviced by personal assistants. Currently, the presented modeling system is deployed over a two layered system infrastructure in the region of Plovdiv city. Plovdiv is the center of vegetable production in Bulgaria. The process of modeling intelligent irrigation systems and the current results are discussed in this paper.</p> </abstract>

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