scholarly journals Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status

Nitrogen ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 67-80
Author(s):  
Mohammad Habibullah ◽  
Mohammad Reza Mohebian ◽  
Raju Soolanayakanahally ◽  
Ali Newaz Bahar ◽  
Sally Vail ◽  
...  
Keyword(s):  
Field Experiment ◽  
Sensor Array ◽  
Near Infrared ◽  
Low Cost ◽  
Leaf Nitrogen ◽  
Greenhouse Experiment ◽  
Nitrogen Status ◽  
N Level ◽  
Sensor Module ◽  
Reflectance Data

A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). We conducted the experiment on canola leaves in a controlled greenhouse environment as well as in the field. In the greenhouse experiment, spectral data were collected from 87 leaves of 24 canola plants, subjected to varying levels of N fertilization. Later, 42 canola cultivars were subjected to low and high nitrogen levels in the field experiment. The k-nearest neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the greenhouse experiment and 79.2% for the field experiment. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf nitrogen status.

Application of a low-cost NDIR sensor module for measurements of in situ soil CO2 concentration

2020 ◽  
Author(s):  
Adrian Heger ◽  
Volker Kleinschmidt ◽  
Alexander Gröngröft ◽  
Lars Kutzbach ◽  
Annette Eschenbach
Keyword(s):  
Field Experiment ◽  
Laboratory Experiment ◽  
Low Cost ◽  
Co2 Concentration ◽  
Soil Conditions ◽  
Data Logging ◽  
Accuracy And Precision ◽  
Sensor Module ◽  
Long Time

<p>We applied the low-cost non-dispersive infrared sensor module K33 (ICB, Senseair, Sweden) for measurements of soil CO<sub>2</sub> concentration. We integrated the sensor module in a new soil probe suitable for in situ measurements of soil gas CO<sub>2</sub> concentration. Therefore, we covered the sensor module with epoxy resin. For continuous measurements, we connected our soil CO<sub>2</sub> probe to a microcontroller (MEGA 2560 Rev3, Arduino.cc, Italy) equipped with a data logging shield (Adalogger FeatherWing, Adafruit, USA). In a laboratory experiment, we evaluated the accuracy and precision of our soil CO<sub>2</sub> probe at changing temperature and humidity by comparison with the often used CO<sub>2</sub> probe GMP343 (Vaisala, Finland) as a reference. In a field experiment, we buried our soil CO<sub>2</sub> probe to test its performance under natural environmental conditions.</p><p>The result of the laboratory experiment is that our soil CO<sub>2</sub> probe compares well with the GMP343, even at maximum relative humidity. The accuracy (<0.1 % CO<sub>2</sub>) was below the accuracy given by the manufacturer. The field experiment demonstrated that our soil CO<sub>2</sub> probe provides high-quality measurements of soil CO<sub>2</sub> concentrations under in situ soil conditions. After retrieving it, it still measured with the same accuracy and precision as before.</p><p>In summary, we used the sensor module K33 for the first time to measure in situ soil CO<sub>2</sub> concentrations by integrating it into a newly developed probe. The cost-efficient availability of our CO<sub>2</sub> probe opens up the opportunity to carry out continuous soil CO<sub>2</sub> measurements over long time periods with simultaneously high spatial resolution.</p>

scholarly journals A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1449 ◽  
Author(s):  
Mohammad Habibullah ◽  
Mohammad Reza Mohebian ◽  
Raju Soolanayakanahally ◽  
Khan A. Wahid ◽  
Anh Dinh
Keyword(s):  
Water Content ◽  
Near Infrared ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Cost Effective ◽  
Leaf Nitrogen ◽  
Sensor System ◽  
Coefficient Of Determination ◽  
Healthy Growth ◽  
Water Contents

Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants—canola, corn, soybean, and wheat—are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination ( R 2 ) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an R 2 of 18.02%, corn showed an R 2 of 68.41%, soybean showed an R 2 of 46.38%, and wheat showed an R 2 of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.

Estimation of Canopy Leaf Nitrogen Status Using Imaging Spectrometer and Digital Camera in Cotton

2011 ◽  
Vol 37 (6) ◽  
pp. 1039-1048 ◽  
Author(s):  
Fang-Yong WANG ◽  
Ke-Ru WANG ◽  
Shao-Kun LI ◽  
Shi-Ju GAO ◽  
Chun-Hua XIAO ◽  
...  
Keyword(s):  
Digital Camera ◽  
Leaf Nitrogen ◽  
Nitrogen Status ◽  
Imaging Spectrometer

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Design, manufacture and testing of a prototype obstacle-detection device at blind corners: an automated highway system technology

2020 ◽  
Author(s):  
Charles Atombo ◽  
Emmanuel Gbey ◽  
Apevienyeku Kwami Holali
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Effective Area ◽  
Obstacle Detection ◽  
The Road ◽  
Vibration Sensors ◽  
Road Signs ◽  
Sensor Module ◽  
Vehicle Path ◽  
Automated Highway

Abstract Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

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