scholarly journals Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 183
Author(s):  
Michele Denora ◽  
Marco Fiorentini ◽  
Stefano Zenobi ◽  
Paola A. Deligios ◽  
Roberto Orsini ◽  
...  
Keyword(s):  
Low Cost ◽  
Optimal Number ◽  
Machine Learning Algorithms ◽  
Yield Data ◽  
Management Zones ◽  
Soil Sensors ◽  
Zone Management ◽  
Gap Statistic ◽  
Vegetation Indexes ◽  
Significant Difference

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

scholarly journals Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods

2020 ◽  
Vol 13 (4) ◽  
pp. 1693-1707 ◽  
Author(s):  
Minxing Si ◽  
Ying Xiong ◽  
Shan Du ◽  
Ke Du
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Random Search ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ambient Conditions ◽  
Test Dataset ◽  
Compact Size ◽  
Significant Difference

Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was colocated with a reference instrument, the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, at the Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms using random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN). Field evaluation showed that the Pearson correlation (r) between the low-cost sensor and the SHARP instrument was 0.78. The Fligner and Killeen (F–K) test indicated a statistically significant difference between the variances of the PM2.5 values by the low-cost sensor and the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F–K test using the test dataset showed that the variances of the PM2.5 values by the NN, XGBoost, and the reference method were not statistically significantly different. From this study, we conclude that a feedforward NN is a promising method to address the poor performance of low-cost sensors for PM2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.

scholarly journals Comparative analysis of soil-sampling methods used in precision agriculture

2021 ◽  
Author(s):  
José Roberto Moreira Ribeiro Gonçalves ◽  
Gabriel Araújo e Silva Ferraz ◽  
Étore Francisco Reynaldo ◽  
Diego Bedin Marin ◽  
Patrícia Ferreira Ponciano Ferraz ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Agricultural Production ◽  
Sampling Methods ◽  
Soil Sampling ◽  
Soil Electrical Conductivity ◽  
Production Environment ◽  
Management Zones ◽  
Grid Sampling ◽  
Soil Sensors ◽  
Significant Difference

The aim of this study was to compare three different soil-sampling methods used in Precision Agriculture and their environmental impact in the agricultural production environment. The sampling methods used were: management zones by elevation (MZA), grid sampling (GS), and sampling oriented by apparent soil electrical conductivity (OS). It was tested in three different fields. When the recommendations were compared, a significant difference among the suggested dosages was observed, indicating the need to improve the soil-sampling techniques, since there were doubts about input deficits or overdoses, regardless of the technology studied. The GS method was the most environmentally viable alternative for P compared to other methods and the OS presented as the better option for K and N. However, the use of soil sensors has been shown to be a viable technology that needs further improvement in order to improve productivity and, hence, economic and environmental gains.

scholarly journals Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Technologies

2019 ◽  
Author(s):  
Minxing Si ◽  
Ying Xiong ◽  
Shan Du ◽  
Ke Du
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Random Search ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ambient Conditions ◽  
Test Dataset ◽  
Compact Size ◽  
Significant Difference

Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of low-cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was co-located with a reference instrument, named Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, in Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine learning algorithms using random search techniques for the best model architectures. The two machine learning algorithms are XGBoost and feedforward neural network (NN). Field evaluation showed that the Pearson r between the low-cost sensor and the SHAPR instrument was 0.78. Fligner and Killeen (F-K) test indicated a statistically significant difference between the variances of the PM2.5 values by the low-cost sensor and by the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset, compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F-K test using the test dataset showed that the variances of the PM2.5 values by the NN and the XGBoost and by the reference method were not statistically significantly different. From this study, we conclude that feedforward NN is a promising method to address the poor performance of the low-cost sensors for PM2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.

scholarly journals OPTIMIZING LOW-COST UAV AERIAL IMAGE MOSAICING FOR CROP GROWTH MONITORING

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 7-12
Author(s):  
P. Bupathy ◽  
R. Sivanpillai ◽  
V. V. Sajithvariyar ◽  
V. Sowmya
Keyword(s):  
Management Practices ◽  
Low Cost ◽  
Optimal Number ◽  
Aerial Image ◽  
Growth Monitoring ◽  
Image Mosaic ◽  
Yield Data ◽  
Matching Process ◽  
Crop Field ◽  
Data Gaps

Abstract. High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions collect hundreds of photos with high overlap and side overlap in order to generate mosaic without data gaps or distortion. These frames are aligned using the location information associated with each image. The same features are identified in multiple frames for generating the mosaic. In this process, it is common to use all or most of the images which requires a lot of resources. Uploading and processing hundreds of images could take several hours to days. Many farmers and crop consultants in developing countries may not have the necessary resources to upload hundreds of images. This study assessed the optimal number of images required to generate an image mosaic for a crop field without any data gaps or distortion. Images were collected at two different heights and directions. First, the mosaic was generated using all (100%) frames followed by subsets containing 90%, through 50% of images. Results obtained will assist us to plan the settings in future flight missions for acquiring optimal number of images required for generating image mosaic.

The system of indicators of the quality of the carbon-carbon composite materials and technologies of their production

2018 ◽  
pp. 127-132
Author(s):  
T. N. Antipova ◽  
D. S. Shiroyan
Keyword(s):  
Composite Material ◽  
Low Cost ◽  
Experimental Studies ◽  
Carbon Matrix ◽  
Carbon Composite ◽  
Optimal Number ◽  
Laboratory Equipment ◽  
Carbon Composite Material ◽  
Carbon Composite Materials

The system of indicators of quality of carbon-carbon composite material and technological operations of its production is proved in the work. As a result of the experimental studies, with respect to the existing laboratory equipment, the optimal number of cycles of saturation of the reinforcing frame with a carbon matrix is determined. It was found that to obtain a carbon-carbon composite material with a low cost and the required quality indicators, it is necessary to introduce additional parameters of the pitch melt at the impregnation stage.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

2021 ◽  
Vol 14 (5) ◽  
pp. 440
Author(s):  
Eirini Siozou ◽  
Vasilios Sakkas ◽  
Nikolaos Kourkoumelis
Keyword(s):  
Infrared Spectroscopy ◽  
Low Cost ◽  
Diclofenac Sodium ◽  
Reference Method ◽  
Sodium Content ◽  
Attenuated Total Reflection ◽  
Accuracy And Precision ◽  
Significant Difference ◽  
Ft Ir

A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples’ concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost.

scholarly journals Can Hematological Parameters Play a Role in the Differential Diagnosis of Adrenal Tumors?

Uro ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 39-44
Author(s):  
Mehmet Gürkan Arıkan ◽  
Göktan Altuğ Öz ◽  
Nur Gülce İşkan ◽  
Necdet Süt ◽  
İlkan Yüksel ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Inflammatory Markers ◽  
Blood Count ◽  
Complete Blood Count ◽  
Low Cost ◽  
Easy Access ◽  
Adrenal Tumors ◽  
Tumor Diameter ◽  
Lymphocyte Ratio ◽  
Significant Difference

There have been few studies reported with conflicting results in the use of neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), redcell-distribution-width (RDW), etc. for predicting prognosis and differential diagnosis of adrenal tumors. The aim of this study is to investigate the role of inflammatory markers through a complete blood count, which is an easy access low-cost method, for the differential diagnosis of adrenocortical adenoma (ACA), adrenocortical carcinoma (ACC), and pheochromocytoma. The data of patients who underwent adrenalectomy between the years of 2010–2020 were retrospectively analyzed. Systemic hematologic inflammatory markers based on a complete blood count such as neutrophil ratio (NR), lymphocyte ratio (LR), NLR, PLR, RDW, mean platelet volume (MPV), and maximum tumor diameter (MTD) were compared between the groups. A statistically significant difference was found between the three groups in terms of PLR, RDW, and MTD. With post-hoc tests, a statistically significant difference was found in PLR and MTD between the ACA and ACC groups. A statistically significant difference was found between the ACA and pheochromocytoma groups in PLR and RDW values. In conclusion, it could be possible to plan a more accurate medical and surgical approach using PLR and RDW, which are easily calculated through an easy access low-cost method such as a complete blood count, together with MTD in the differential diagnosis of ACC, ACA, and pheochromocytoma.

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

Identifying potential soybean management zones from multi-year yield data

2005 ◽  
Vol 46 (1-3) ◽  
pp. 309-327 ◽  
Author(s):  
Dan B. Jaynes ◽  
Tom S. Colvin ◽  
Tom C. Kaspar
Keyword(s):  
Yield Data ◽  
Management Zones
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