Low-cost assessment of wheat resistance to yellow rust through conventional RGB images

2015 ◽  
Vol 116 ◽  
pp. 20-29 ◽  
Author(s):  
B. Zhou ◽  
A. Elazab ◽  
J. Bort ◽  
O. Vergara ◽  
M.D. Serret ◽  
...  
Keyword(s):  
Yellow Rust ◽  
Low Cost ◽  
Cost Assessment ◽  
Rgb Images

Low-cost assessment of grain yield in durum wheat using RGB images

2019 ◽  
Vol 105 ◽  
pp. 146-156 ◽  
Author(s):  
Jose A. Fernandez-Gallego ◽  
Shawn C. Kefauver ◽  
Thomas Vatter ◽  
Nieves Aparicio Gutiérrez ◽  
María Teresa Nieto-Taladriz ◽  
...  
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Low Cost ◽  
Cost Assessment ◽  
Rgb Images

scholarly journals Generating Super-Resolved Depth Maps Using Low-Cost Sensors and RGB Images

2014 ◽  
pp. 632-641 ◽  
Author(s):  
Leandro Tavares Aragão dos Santos ◽  
Manuel Eduardo Loaiza Fernandez ◽  
Alberto Barbosa Raposo
Keyword(s):  
Low Cost ◽  
Depth Maps ◽  
Rgb Images

scholarly journals Automated movement assessment in stroke rehabilitation

2021 ◽  
Author(s):  
Tamim Ahmed ◽  
Kowshik Thopalli ◽  
Thanassis Rikakis ◽  
Pavan Turaga ◽  
Aisling Kelliher ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Hierarchical Model ◽  
Stroke Rehabilitation ◽  
Expert Knowledge ◽  
Low Cost ◽  
Data Driven ◽  
Task Completion ◽  
Convolutional Network ◽  
Rgb Images ◽  
High Level

We are developing a system for long-term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high-level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high-level priors to data-driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data-driven techniques. We use a transformer-based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complementary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce robust segmentation and task assessment results on noisy, variable, and limited data, which is characteristic of low-cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification, and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).

scholarly journals An alternative approach to measuring demand changes in meat markets

2019 ◽  
Vol 22 (3) ◽  
pp. 397-412 ◽  
Author(s):  
Anton Bekkerman ◽  
Gary W. Brester ◽  
Glynn T. Tonsor
Keyword(s):  
Theoretical Foundation ◽  
Low Cost ◽  
Equilibrium Price ◽  
Cost Assessment ◽  
Alternative Approach ◽  
Empirical Performance ◽  
Demand Shifts ◽  
Meat Markets ◽  
Demand Changes ◽  
Changes Over Time

Commodity groups, academics, government agencies, and marketing analysts often have strong interests in understanding changes in demand for products. It is often the case, however, that only equilibrium price and quantity data are available for identifying changes in demand. But, such equilibria are the result of both changes in demand and changes in supply – the latter of which causes changes in quantity demanded. Although an existing index-based method is widely used to identify demand shifts, we consider its theoretical foundation and empirical performance against a proposed alternative. We find that when using widely available but highly aggregated annual-level price and quantity data, our alternative better characterizes demand shifts for goods such as beef, pork, poultry, and lamb. For many agribusinesses that require information about market dynamics in their industry, our method is likely to provide a more accurate, low-cost assessment of demand changes over time.

scholarly journals Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 285 ◽  
Author(s):  
Salima Yousfi ◽  
Adrian Gracia-Romero ◽  
Nassim Kellas ◽  
Mohamed Kaddour ◽  
Ahmed Chadouli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Water Status ◽  
Low Cost ◽  
Vegetation Indices ◽  
Carbon Isotopic Composition ◽  
Canopy Temperature ◽  
N Fertilization ◽  
Canopy Area ◽  
Rgb Images

Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

scholarly journals Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 79
Author(s):  
Lorena Parra ◽  
David Mostaza-Colado ◽  
Salima Yousfi ◽  
Jose F. Marin ◽  
Pedro V. Mauri ◽  
...  
Keyword(s):  
Cell Size ◽  
Low Cost ◽  
Information Source ◽  
Initial Approximation ◽  
Flying Height ◽  
Training Dataset ◽  
Establishment Success ◽  
Aggregation Techniques ◽  
A Cell ◽  
Rgb Images

The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture’s size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species.

scholarly journals CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 349-356 ◽  
Author(s):  
M. Hassanein ◽  
M. Khedr ◽  
N. El-Sheimy
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Weed Management ◽  
Low Cost ◽  
Color Space ◽  
Orientation Angle ◽  
Weed Detection ◽  
Agriculture Industry ◽  
Rgb Images ◽  
Scan Line

<p><strong>Abstract.</strong> Precision Agriculture (PA) management systems are considered among the top ten revolutions in the agriculture industry during the last couple decades. Generally, the PA is a management system that aims to integrate different technologies as navigation and imagery systems to control the use of the agriculture industry inputs aiming to enhance the quality and quantity of its output, while preserving the surrounding environment from any harm that might be caused due to the use of these inputs. On the other hand, during the last decade, Unmanned Aerial Vehicles (UAVs) showed great potential to enhance the use of remote sensing and imagery sensors for different PA applications such as weed management, crop health monitoring, and crop row detection. UAV imagery systems are capable to fill the gap between aerial and terrestrial imagery systems and enhance the use of imagery systems and remote sensing for PA applications. One of the important PA applications that uses UAV imagery systems, and which drew lots of interest is the crop row detection, especially that such application is important for other applications such as weed detection and crop yield predication. This paper introduces a new crop row detection methodology using low-cost UAV RGB imagery system. The methodology has three main steps. First, the RGB images are converted into HSV color space and the Hue image are extracted. Then, different sections are generated with different orientation angles in the Hue images. For each section, using the PCA of the Hue values in the section, an analysis can be performed to evaluate the variances of the Hue values in the section. The crop row orientation angle is detected as the same orientation angle of the section that provides the minimum variances of Hue values. Finally, a scan line is generated over the Hue image with the same orientation angle of the crop rows. The scan line computes the average of the Hue values for each line in the Hue image similar to the detected crop row orientation. The generated values provide a graph full of peaks and valleys which represent the crop and soil rows. The proposed methodology was evaluated using different RGB images acquired by low-cost UAV for a Canola field. The images were taken at different flight heights and different dates. The achieved results proved the ability of the proposed methodology to detect the crop rows at different cases.</p>

Assessment Tools

2020 ◽  
pp. 285-292
Author(s):  
Eric A. Youngstrom ◽  
Jessica A. Janos ◽  
Joshua A. Langfus
Keyword(s):  
Treatment Process ◽  
Low Cost ◽  
Treatment Plan ◽  
Bipolar Disorders ◽  
Assessment Tools ◽  
Cost Assessment ◽  
Proper Treatment ◽  
Current State ◽  
Specific Setting ◽  
Treatment Plans

Bipolar disorders are difficult to diagnose and treat, despite their global prevalence and pervasiveness. With the proper tools, however, clinicians and researchers alike are able to detect bipolar disorders in their patients and establish the proper treatment plans. Knowing the prevalence of bipolar disorders and other common diagnoses in a specific setting, gathering predictive information before the first visit, and screening patients with efficient, low-cost assessment options are a few of the ways that clinicians can be better prepared to detect bipolar disorders in their patients. Further, assessment should not halt once a diagnosis is established; brief, recurring measures to collect data about a patient’s current state throughout treatment offer important information about symptoms, progress, and how a treatment plan can be tailored to meet a client’s ongoing needs. This chapter equips clinicians and researchers with the tools to confidently diagnose their patients with bipolar disorders, suggesting tips to establish diagnostic hypotheses as well as specific assessments for both adults and youths for whom the diagnosis seems likely. Technology in particular offers the opportunity to access low-cost assessment options and administer ongoing measures to ensure that clinicians continue to meet their patients’ needs throughout the treatment process.

Thermal rehabilitation of Romanian housing: a low cost assessment tool

2012 ◽  
Vol 5 (3) ◽  
pp. 235-243
Author(s):  
N. Cobîrzan ◽  
C. Oltean-Dumbrava ◽  
M. Brumaru
Keyword(s):  
Assessment Tool ◽  
Low Cost ◽  
Cost Assessment
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