Low-Power FPGA Architecture Based Monitoring Applications in Precision Agriculture

2021 ◽  
Vol 11 (4) ◽  
pp. 39
Author(s):  
Amine Saddik ◽  
Rachid Latif ◽  
Abdelhafid El Ouardi
Keyword(s):  
Precision Agriculture ◽  
Agricultural Fields ◽  
Vegetation Monitoring ◽  
Computing Power ◽  
Monitoring Applications ◽  
Aerial Vehicle ◽  
On Chip ◽  
The Right ◽  
Power And Energy ◽  
Monitoring Algorithm

Today’s on-chip systems technology has grounded impressive advances in computing power and energy consumption. The choice of the right architecture depends on the application. In our case, we were studying vegetation monitoring algorithms in precision agriculture. This study presents a system based on a monitoring algorithm for agricultural fields, an electronic architecture based on a CPU-FPGA SoC system and the OpenCL parallel programming paradigm. We focused our study on our own dataset of agricultural fields to validate the results. The fields studied in our case are in the Guelmin-Oued noun region in the south of Morocco. These fields are divided into two areas, with a total surface of 3.44 Ha2 for the first field and 3.73 Ha2 for the second. The images were collected using a DJI-type unmanned aerial vehicle and an RGB camera. Performance evaluation showed that the system could process up to 86 fps versus 12 fps or 20 fps in C/C++ and OpenMP implementations, respectively. Software optimizations have increased the performance to 107 fps, which meets real-time constraints.

scholarly journals QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 952
Author(s):  
Lia Duarte ◽  
Ana Cláudia Teodoro ◽  
Joaquim J. Sousa ◽  
Luís Pádua
Keyword(s):  
Open Source ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Data Interpretation ◽  
Vegetation Monitoring ◽  
Distribution Maps ◽  
Monitoring Process ◽  
Multi Temporal ◽  
The Creation ◽  
The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

scholarly journals A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3751 ◽  
Author(s):  
Rico Valentino ◽  
Woo-Sung Jung ◽  
Young-Bae Ko
Keyword(s):  
Response Time ◽  
Computing Time ◽  
Energy Resources ◽  
Computation Offloading ◽  
Computing Power ◽  
Packet Delivery ◽  
Time Prediction ◽  
Prediction Module ◽  
Aerial Vehicle ◽  
Power And Energy

Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

scholarly journals Feasibility of Quantum Computers in Cryogenic Systems

2020 ◽  
Vol 9 (01) ◽  
pp. 24919-24920
Author(s):  
Viplove Divyasheesh ◽  
Rakesh Jain
Keyword(s):  
Quantum Computers ◽  
Computing Power ◽  
Quantum Bits ◽  
Silicon Chips ◽  
Cool Environment ◽  
Quantum Processor ◽  
Quantum Phenomena ◽  
The Right ◽  
Electronic Controller ◽  
And Control

Quantum computers consist of a quantum processor – sets of quantum bits or qubits operating at an extremely low temperature – and a classical electronic controller to read out and control the processor. The machines utilize the unusual properties of matter at extremely small scales – the fact that a qubit, can represent “1” and “0” at the same time, a phenomenon known as superposition. (In traditional digital computing, transistors in silicon chips can exist in one of two states represented in binary by a 1 or 0 not both). Under the right conditions, computations carried out with qubits are equivalent to numerous classical computations performed in parallel, thus greatly enhancing computing power compared to today’s powerful supercomputers and the ability to solve complex problems without the sort of experiments necessary to generate quantum phenomena. this technology is unstable and needs to be stored in a cool environment for faster and more secure operation.In this paper, we discuss the possibility of integrating quantum computers with electronics at deep cryogenic temperatures.  

scholarly journals Methodology for Indoor Positioning and Landing of an Unmanned Aerial Vehicle in a Smart Manufacturing Plant for Light Part Delivery

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1680
Author(s):  
Pedro Orgeira-Crespo ◽  
Carlos Ulloa ◽  
Guillermo Rey-Gonzalez ◽  
José Antonio Pérez García
Keyword(s):  
Vision System ◽  
Indoor Positioning ◽  
Smart Manufacturing ◽  
Manufacturing Plant ◽  
Wireless Mesh ◽  
Industrial Internet ◽  
Last Mile ◽  
Aerial Vehicle ◽  
On Chip ◽  
Valid Solution

Unmanned aerial vehicles (UAV) are spreading their usage in many areas, including last-mile distribution. In this research, a UAV is used for performing light parts delivery to workstation operators within a manufacturing plant, where GPS is no valid solution for indoor positioning. A generic localization solution is designed to provide navigation using RFID received signal strength measures and sonar values. A system on chip computer is onboarded with two missions: first, compute positioning and provide communication with backend software; second, provide an artificial vision system that cooperates with UAV’s navigation to perform landing procedures. An Industrial Internet of Things solution is defined for workstations to allow wireless mesh communication between the logistics vehicle and the backend software. Design is corroborated through experiments that validate planned solutions.

scholarly journals Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

2019 ◽  
Vol 11 (22) ◽  
pp. 2678 ◽  
Author(s):  
Zhu ◽  
Sun ◽  
Peng ◽  
Huang ◽  
Li ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spatial Scales ◽  
Point Clouds ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
3D Point Clouds ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Multispectral Camera

Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.

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