scholarly journals Nitrogen Content Estimation of Rice Crop Based on Near Infrared (NIR) Reflectance Using Artificial Neural Network (ANN)

2016 ◽  
Vol 33 ◽  
pp. 63-69 ◽  
Author(s):  
Setia Darmawan Afandi ◽  
Yeni Herdiyeni ◽  
Lilik B. Prasetyo ◽  
Wahyudi Hasbi ◽  
Kohei Arai ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nitrogen Content ◽  
Near Infrared ◽  
Rice Crop ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

2020 ◽  
Author(s):  
Masabho P. Milali ◽  
Samson S. Kiware ◽  
Nicodem J. Govella ◽  
Fredros Okumu ◽  
Naveen Bansal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Burkina Faso ◽  
Near Infrared ◽  
Animal Blood ◽  
Near Infrared Spectra ◽  
Out Of Sample ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractBackgroundAfter mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases to humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which currently are used to determine the parity status of mosquitoes, are very tedious and limited to very few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes.Methods and resultsIn this study, we train artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae collected from Muleba, Tanzania (Muleba-GA); An. gambiae collected from Burkina Faso (Burkina-GA); and An.gambiae from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9 ± 2.8% (N=927), 68.7 ± 4.8% (N=140), 80.3 ± 2.0% (N=158), and 75.7 ± 2.5% (N=298), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 ± 2.2%, (N=927), 89.8 ± 1.7% (N=140), 93.3 ± 1.2% (N=158), and 92.7 ± 1.8% (N=298) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively.ConclusionThese results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

scholarly journals Artificial neural network and partial least square in predicting blood hemoglobin using near-infrared spectrum

2019 ◽  
Vol 16 (2) ◽  
pp. 701
Author(s):  
Mohd Nazrul Effendy Mohd Idrus ◽  
Kim Seng Chia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Latent Variables ◽  
Near Infrared ◽  
Spectroscopic Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Predictive models is crucial in near-infrared (NIR) spectroscopic analysis. Partial least square - artificial neural network (PLS-ANN) is a hybrid method that may improve the performance of prediction in NIR spectroscopic analysis. This study investigates the advantage of PLS-ANN over the well-known modelling in spectroscopy analysis that is partial least square (PLS) and artificial neural network (ANN). The results show that ANN that coupled with first order SG derivatives achieved the best prediction with root mean square error of prediction (RMSEP) of 0.3517 gd/L and coefficient of determination ( ) of 0.9849 followed by PLS-ANN with RMSEP of 0.4368 gd/L and  of 0.9787, and PLS with RMSEP of 0.4669 gd/L and  of 0.9727. This suggests that the spectrum information may unable to be totally represented by the first few latent variables of PLS and a nonlinear model is crucial to model these nonlinear information in NIR spectroscopic analysis.</p>

scholarly journals Classification of RRIM clone series using artificial neural network

2020 ◽  
Vol 9 (2) ◽  
pp. 297
Author(s):  
Faridatul Ama Ismail ◽  
Nina Korlina Madzhi ◽  
Noor Ezan Abdullah ◽  
Hadzli Hashim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Infrared ◽  
Input Parameter ◽  
Back Propagation ◽  
Comparative Investigation ◽  
Sensing Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents comparative investigation on the classification of rubber latex clone series using Artificial Neural Network (ANN) based on optical sensing technique. Rubber Research Institute of Malaysia (RRIM) introduced the rubber breeding program known as RRIM clone series in order to increase the yield of latex production and the rubber wood to meet the requirement for export and import in upstream sector. Due to the large numbers of clones launched with different characteristics and properties, this bring difficulty such as lack of information regarding to the identification on cloning. Therefore, this work developed an optical based sensing system for classification of the selected RRIM 2000 and 3000 clone series based. Near Infrared Sensors was used as sensing element in order to measure the latex from the top surface and photodiode which received the reflected light from the sensor via reflectance index in term of voltage. The raw obtained data was then used as input parameter for ANN tool which supervised by scaled gradient back propagation and the performance was optimized at 25 neurons with 74.4% accuracy. By using ANN the sensitivity, specificity and accuracy for each clones are measured.  RRIM 3001 shows the highest sensitivity, 94.1% while RRIM 2002 shows the highest specificity of 99.1% accuracy, 93.1%. As a result, the system could differentiate RRIM 2002 more compare to other clones.

Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand)

Soil Research ◽  
2003 ◽  
Vol 41 (1) ◽  
pp. 47 ◽  
Author(s):  
K. W. Daniel ◽  
N. K. Tripathi ◽  
K. Honda
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Infrared ◽  
Soil Nutrient ◽  
Bare Soil ◽  
Absorption Bands ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
In Situ Spectra

Reflectance spectrometry is an emerging and non-destructive detection technique bearing fast, cheap, and accurate results compared with conventional assessments. Most field and laboratory-based spectrometers are restricted to VNIR (visible–near-infrared). However, soils fail to show well-defined narrow absorption bands in this region. This obstructs the use of curve feature as a diagnostic criterion for soil nutrient predictions. In this paper artificial neural network (ANN) is implemented to estimate soil organic matter, phosphorous, and potassium from the VNIR spectrum (400–1100 nm). Macronutrients were modelled from 41 bare soil reflectances of Lop Buri province, Thailand. Neurons were trained from 7 bandwidth categories derived from laboratory-based StellarNet spectroradiometer and in situ photometer. Satisfactory results were attained and compared across different synthesised bandwidths. Models exhibited slightly better estimates from the laboratory than in situ spectra, and from narrower than broader bandwidths. Widening bandwidth corresponds with attenuated predictive powers, coupled with rising errors. Cross validation of models yielded acceptable correlations. The strength of models confirmed the capability of ANN to estimate macronutrients by solving difficulties incurred from high cross-channel correlations prevailing in conventional statistical techniques.

Application of an Artificial Neural Network to the Identification of Amino Acids from near Infrared Spectral Data

1993 ◽  
Vol 1 (4) ◽  
pp. 199-208 ◽  
Author(s):  
Tetsuo Sato
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Artificial Neural Network ◽  
Spectral Data ◽  
Near Infrared ◽  
Principal Component ◽  
Mathematical Treatment ◽  
Second Derivative ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was trained to identify a group of amino acids from near infrared (NIR) spectral data. The input, the hidden and the output layers were composed of 701 (for raw spectral data, fixed) or 324 (for second derivative of the spectral data, fixed) units, 1 to 100 (changeable) units and 20 (fixed) units, respectively. Using the raw spectral data, the ANN did not converge to a suitable error level. However, when the second derivative spectra were used, whether original or standardised spectra, the error reduced to a suitable level, because this mathematical treatment made their differences in NIR spectra clearer. The ANN was trained for non-pretreated amino acids and then applied to the other prediction sets. When standardised spectra were used, the ANN could almost correctly identify the amino acids not only for non-pretreated amino acids but also for ground samples or samples from different batches. The results obtained by principal component analysis (PCA) were also compared with those by the ANN.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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