scholarly journals Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 33 ◽  
Author(s):  
Thomaz W. F. Xavier ◽  
Roberto N. V. Souto ◽  
Thiago Statella ◽  
Rafael Galbieri ◽  
Emerson S. Santos ◽  
...  
Keyword(s):  
Production Cost ◽  
Low Cost ◽  
Classification Performance ◽  
Leaf Blight ◽  
Multispectral Imagery ◽  
Kappa Index ◽  
Pesticide Application ◽  
Flight Height ◽  
Farm Scale ◽  
Airborne Vehicle

The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.

scholarly journals Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests

2020 ◽  
Author(s):  
Valter Augusto de Freitas Barbosa ◽  
Juliana Carneiro Gomes ◽  
Maíra Araújo de Santana ◽  
Jeniffer Emídio de Almeida Albuquerque ◽  
Rodrigo Gomes de Souza ◽  
...  
Keyword(s):  
Influenza A ◽  
Intelligent System ◽  
Low Cost ◽  
Hematological Parameters ◽  
Rapid Test ◽  
Albert Einstein ◽  
Classification Performance ◽  
Kappa Index ◽  
Blood Tests ◽  
Intelligent Software

Abstract A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Due to this fact, it is necessary quick and precise easily available diagnosis tests. The current Covid-19 diagnosis benchmark is RT-PCR with DNA identification, but its results takes too long to be available. Tests based on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low when viral charge is reduced. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We employed a dataset provided by Hospital Israelita Albert Einstein, a Brazilian private hospital. The database contains the results of more than one hundred laboratory exams, such as blood count, tests for the presence of viruses such as influenza A, and urine tests, of 5644 patients. Among these patients, 559 of them are infected with SARS-Cov2. We used metaheuristics algorithms to reduce the set of We tested several machine learning methods, and we achieved high classification performance: 95.159% +- 0.693 of overall accuracy, kappa index of 0.903 +- 0.014, sensitivity of 0.968 +- 0.007, precision of 0.938 +- 0.010, and specificity of 0.936 +- 0.011. Experimental results pointed out to Bayes Network as the best configuration. In addition, only 24 blood tests were needed. This points to the possibility of a new low cost rapid test based on common blood exams and intelligent software. The desktop version of the system is fully functional and available for free use.

scholarly journals Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests

2020 ◽  
Author(s):  
Valter Augusto de Freitas Barbosa ◽  
Juliana Carneiro Gomes ◽  
Maira Araujo de Santana ◽  
Jeniffer Emidio de Almeida Albuquerque ◽  
Rodrigo Gomes de Souza ◽  
...  
Keyword(s):  
Intelligent System ◽  
Low Cost ◽  
Hematological Parameters ◽  
Computational Cost ◽  
Rapid Test ◽  
Classification Performance ◽  
Kappa Index ◽  
Blood Tests ◽  
Bayes Network ◽  
Precise Diagnosis

A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Because of this, it is necessary quick and precise diagnosis test. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results takes too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We tested several machine learning methods, and we achieved high classification performance: 95.159% +- 0.693 of overall accuracy, kappa index of 0.903 +- 0.014, sensitivity of 0.968 +- 0.007, precision of 0.938 +- 0.010 and specificity of 0.936 +- 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.

Low-cost system for radiometric calibration of UAV-based multispectral imagery

2020 ◽  
pp. 1-15
Author(s):  
Jorge Tadeu Fim Rosas ◽  
Francisco de Assis de Carvalho Pinto ◽  
Daniel Marçal de Queiroz ◽  
Flora Maria de Melo Villar ◽  
Rodrigo Nogueira Martins ◽  
...  
Keyword(s):  
Low Cost ◽  
Multispectral Imagery ◽  
Radiometric Calibration ◽  
Cost System

scholarly journals Shallow Water Bathymetry Inversion Using Hybrid Multi-band Composites Resulted from Integration of Drone-based RGB and Multispectral Imagery

2022 ◽  
Author(s):  
Evangelos Alevizos ◽  
Athanasios V Argyriou ◽  
Dimitris Oikonomou ◽  
Dimitrios D Alexakis
Keyword(s):  
Low Cost ◽  
Visible Spectrum ◽  
Small Scale ◽  
Multispectral Imagery ◽  
Depth Data ◽  
Recent Developments ◽  
Model Tuning ◽  
Inversion Techniques ◽  
Camera Sensors ◽  
Current Inversion

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum is available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery, as an alternative to costly hyperspectral sensors. Combining drone-based RGB and multispectral imagery into a single cube dataset, provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types. The use of suitable end-member spectra which are representative of the seafloor types of the study area and the sun zenith angle are important parameters in model tuning. The results of this study show good correlation (R2>0.7) and less than half a meter error when they are compared with sonar depth data. Consequently, integration of various drone-based imagery may be applied for producing centimetre resolution bathymetry maps at low cost for small-scale shallow areas.

scholarly journals Pendugaan Intensitas Serangan Penyakit BLB (Bacterial Leaf Blight) pada Tanaman Padi melalui Pendekatan Citra Multispektral

2020 ◽  
Vol 8 (2) ◽  
pp. 338
Author(s):  
Gusti Bagus Eka Chandra ◽  
I Made Anom S. Wijaya ◽  
Yohanes Setiyo
Keyword(s):  
Technology Development ◽  
Leaf Blight ◽  
Multispectral Image ◽  
Bacterial Leaf Blight ◽  
Estimation Accuracy ◽  
Multispectral Images ◽  
Multispectral Imagery ◽  
Rice Plants ◽  
Red Edge ◽  
Disease Attack

ABSTRAK Penyakit Bacterial Leaf Blight (BLB) merupakan salah satu penyakit yang berbahaya bagi tanaman padi. Penyakit ini bisa menyerang di setiap fase pertumbuhan. Perhitungan intensitas serangan penyakit BLB saat ini masih dilakukan secara manual. Diperlukan pengembangan teknologi dalam pendugaan intensitas serangan penyakit BLB melalui citra multispektral. Penelitian ini bertujuan untuk (1) untuk mendapatkan nilai korelasi terbaik antara intensitas serangan penyakit BLB dengan parameter citra multispektral (2) Untuk mendapatkan persamaan pendugaan intensitas serangan penyakit BLB berdasarkan pendekatan citra multispektral. Drone DJI Inspire 1 dengan kamera multispektral digunakan untuk menangkap gambar petak padi. Pengolahan data citra multispektral menggunakan Agisoft Photoscan dan software QGIS 3.8. Berdasarkan dari hasil akuisisi, citra multispektral menghasilkan citra band red, NIR, green, red edge, RGB yang kemudian diolah menjadi transformasi citra NDVI, EVI, dan NDRE. Dari ketiga parameter citra multispektral, nilai NDVI memiliki tingkat korelasi yang lebih kuat dengan koefisien determinasi sebesar 97,5% dan menghasilkan persamaan linier sebagai berikut y = -419,6 + 169,3. Dalam perhitungan nilai eror parameter NDVI memilikinilai eror paling rendah dibandingkan parameter EVI dan NDRE yaitu sebesar 4,64% dengan akurasi pendugaan 95,36%. Citra multispektral dapat digunakan dalam pendugaan intensitas serangan penyakit BLB pada tanaman padi karena menghasilkan nilai korelasi yang sangat kuat, dan akurasi pendugaan yang tinggi dengan nilai eror yang rendah tidak melebihi 10%. ABSTRACT  Bacterial Leaf Blight (BLB) is a disease that is dangerous for rice plants. This disease can attack in every phase of growth. Calculation of BLB disease attack intensity is currently still used manually method. Technology development is needed in estimating the intensity of BLB disease through multispectral imagery. This study aims (1) to get the best correlation value between the intensity of BLB disease attack with multispectral image parameters (2) to get the equation for estimating the intensity of BLB based on multispectral images parameter. Drone DJI Inspire 1 with a multispectral camera is used to captured the paddy field. The captured images was processed using Agisoft Photoscan and QGIS 3.8 software. Based on the results of the acquisition, multispectral images produce red, NIR, green, red edge, RGB band images which were then transformed into NDVI, EVI, and NDRE images. Of the three multispectral image parameters, NDVI values ??have a stronger correlation level with a determination coefficient of 97.5% and produce the following linear equation y = -419.6 + 169.3. In calculating the NDVI parameter error value has the lowest error value compared to the EVI and NDRE parameters which is 4.64% with an accuracy estimate of 95.36%. Multispectral imagery can be used in estimating the intensity of BLB disease attacks in rice plants because it produces a very strong correlation value, and high estimation accuracy with a low error value does not exceed 10%.

scholarly journals Predicting Soil Organic Carbon and Total Nitrogen at the Farm Scale Using Quantitative Color Sensor Measurements

Agronomy ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 212 ◽  
Author(s):  
Roxanne Stiglitz ◽  
Elena Mikhailova ◽  
Julia Sharp ◽  
Christopher Post ◽  
Mark Schlautman ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Low Cost ◽  
Rapid Assessment ◽  
Soil Health ◽  
Sensor Technology ◽  
P Value ◽  
Color Sensor ◽  
Farm Scale

Sensor technology can be a reliable and inexpensive means of gathering soils data for soil health assessment at the farm scale. This study demonstrates the use of color system readings from the Nix ProTM color sensor (Nix Sensor Ltd., Hamilton, ON, Canada) to predict soil organic carbon (SOC) as well as total nitrogen (TN) in variable, glacial till soils at the 147 ha Cornell University Willsboro Research Farm, located in Upstate New York, USA. Regression analysis was conducted using the natural log of SOC (lnSOC) and the natural log of TN (lnTN) as dependent variables, and sample depth and color data were used as predictors for 155 air dried soil samples. Analysis was conducted for combined samples, Alfisols, and Entisols as separate sample sets and separate models were developed using depth and color variables, and color variables only. Depth and L* were significant predictors of lnSOC and lnTN for all sample sets. The color variable b* was not a significant predictor of lnSOC for any soil sample set, but it was for lnTN for all sample sets. The lnSOC prediction model for Alfisols, which included depth, had the highest R2 value (0.81, p-value < 0.001). The lnSOC model for Entisols, which contained only color variables, had the lowest R2 (0.62, p-value < 0.001). The results suggest that the Nix ProTM color sensor is an effective tool for the rapid assessment of SOC and TN content for these soils. With the accuracy and low cost of this sensor technology, it will be possible to greatly increase the spatial and temporal density of SOC and TN estimates, which is critical for soil management.

scholarly journals A user-centered approach for detecting emotions with low-cost sensors

2020 ◽  
Vol 79 (47-48) ◽  
pp. 35885-35907
Author(s):  
Rita Francese ◽  
Michele Risi ◽  
Genoveffa Tortora
Keyword(s):  
Artificial Intelligence ◽  
Detection System ◽  
Low Cost ◽  
Affective State ◽  
Classification Performance ◽  
Emotion Detection ◽  
Emotion Classification ◽  
User Perceptions ◽  
Machine Learning Classifiers ◽  
User Centered

AbstractDetecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.

scholarly journals Techno-Economic Bottlenecks of the Fungal Pretreatment of Lignocellulosic Biomass

Fermentation ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 30 ◽  
Author(s):  
Juliana Vasco-Correa ◽  
Ajay Shah
Keyword(s):  
Corn Stover ◽  
Production Cost ◽  
Low Cost ◽  
Economic Feasibility ◽  
Perennial Grasses ◽  
Process Improvements ◽  
Current State ◽  
Lignocellulosic Feedstocks ◽  
Fungal Pretreatment ◽  
Wastewater Generation

Fungal pretreatment is a biological process that uses rotting fungi to reduce the recalcitrance and enhance the enzymatic digestibility of lignocellulosic feedstocks at low temperature, without added chemicals and wastewater generation. Thus, it has been presumed to be low cost. However, fungal pretreatment requires longer incubation times and generates lower yields than traditional pretreatments. Thus, this study assesses the techno-economic feasibility of a fungal pretreatment facility for the production of fermentable sugars for a 75,700 m3 (20 million gallons) per year cellulosic bioethanol plant. Four feedstocks were evaluated: perennial grasses, corn stover, agricultural residues other than corn stover, and hardwood. The lowest estimated sugars production cost ($1.6/kg) was obtained from corn stover, and was 4–15 times as much as previous estimates for conventional pretreatment technologies. The facility-related cost was the major contributor (46–51%) to the sugar production cost, mainly because of the requirement of large equipment in high quantities, due to process bottlenecks such as low sugar yields, low feedstock bulk density, long fungal pretreatment times, and sterilization requirements. At the current state of the technology, fungal pretreatment at biorefinery scale does not appear to be economically feasible, and considerable process improvements are still required to achieve product cost targets.

A Novel Building Thermal Insulation Material: Expanded Perlite Modified by Aerogel

2011 ◽  
Vol 250-253 ◽  
pp. 507-512
Author(s):  
Zi Sheng Wang ◽  
Hao Chi Tu ◽  
Jin Xiu Gao ◽  
Guo Dong Qian ◽  
Xian Ping Fan ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Mechanical Property ◽  
Thermal Insulation ◽  
Production Cost ◽  
Building Materials ◽  
Low Cost ◽  
Insulation Material ◽  
Expanded Perlite ◽  
Thermal Insulation Material ◽  
Supercritical Fluid Drying

Aerogel is regarded as one kind of super thermal insulation materials which could be large-scalely used as building materials. However, the aerogel’s production cost and poor mechanical property limit the its applications. In this paper, we put forward a new low cost way to produce a novel building thermal insulation material: synthesized the aerogel within the expanded perlite’s pores, and using sodium silicate as precursor without adopting supercritical fluid drying and surface modification. The thermal conductivity of expanded perlite was successfully decreased after modified by aerogel.

scholarly journals Langstroth hive construction with cement-vermiculite

2004 ◽  
Vol 61 (6) ◽  
pp. 573-578 ◽  
Author(s):  
Maria Cristina Affonso Lorenzon ◽  
Rodolfo Gonçalves Cidreira ◽  
Edmundo Henrique Ventura Rodrigues ◽  
Milton Sérgio Dornelles ◽  
Geraldo Pereira Jr
Keyword(s):  
Thermal Conductivity ◽  
Honey Bees ◽  
Production Cost ◽  
Low Cost ◽  
Thermal Control ◽  
Light Weight ◽  
Control Material ◽  
Insulating Material ◽  
Randomized Design ◽  
Completely Randomized Design

Exfoliated vermiculite is a light-weight and cheap product that, because of its thermal resistance, has become a valuable insulating material. With regard to its use in beekeeping, this research tested whether the box for honey bees constructed with cement-vermiculite mortar (CVM) presents physical characteristics similar to those of wood. The experiment was carried out at Seropédica, RJ, Brazil, for eight months. The cement-vermiculite mortar was compared with a control material (pinewood), in the construction of Langstroth boxes and boards, in a completely randomized design, with respect to thermal control, thermal conductivity and its capacity to absorb and lose water. The production cost for a CVM box was estimated. There were no internal temperature differences between CVM and wooden boxes. Thermal conductivity values for CVM and pinewood were similar. CVM absorbed more water and lost water faster than pinewood. Since CVM boxes can be easily constructed, at a low cost and with similar characteristics as traditional boxes, made of wood, the material can be recommended for use in non-migratory beekeeping.

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