Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques
The grain moisture content at harvest time is a key factor that limits harvesting windows. The present study aimed to develop a new methodology to predict wheat moisture content by using multi-layer perceptron (MLP) and support vector regression (SVR) techniques. Five input variables included the nu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-12-01
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| Series: | Information Processing in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317319301696 |
| _version_ | 1797725133038682112 |
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| author | Shamsollah Abdollahpour Armaghan Kosari-Moghaddam Mohammad Bannayan |
| author_facet | Shamsollah Abdollahpour Armaghan Kosari-Moghaddam Mohammad Bannayan |
| author_sort | Shamsollah Abdollahpour |
| collection | DOAJ |
| description | The grain moisture content at harvest time is a key factor that limits harvesting windows. The present study aimed to develop a new methodology to predict wheat moisture content by using multi-layer perceptron (MLP) and support vector regression (SVR) techniques. Five input variables included the number of days after sowing, air temperature, air relative humidity, wind speed on an hourly basis, and precipitation on a 6-hour basis. The study area was Sari County located in the north of Iran. Data were collected from field experiments in two crop years (2016/17 and 2017/18). The results indicated that the developed MLP model outperformed the SVR model in determining wheat moisture content by R2 and RMSE value of 0.92 and 2.09% (wet basis) against 0.79 and 3.09%, respectively. In conclusion, the developed MLP model can be considered a useful method to estimate wheat moisture content at harvest time. |
| first_indexed | 2024-03-12T10:26:46Z |
| format | Article |
| id | doaj.art-6ac2642b417d4eff9b569487c7f5dcd8 |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T10:26:46Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-6ac2642b417d4eff9b569487c7f5dcd82023-09-02T09:38:34ZengElsevierInformation Processing in Agriculture2214-31732020-12-0174500510Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniquesShamsollah Abdollahpour0Armaghan Kosari-Moghaddam1Mohammad Bannayan2Department of Biosystems Engineering, University of Tabriz, Tabriz, Iran; Corresponding author.Department of Biosystems Engineering, University of Tabriz, Tabriz, IranDepartment of Agronomy, Ferdowsi University of Mashhad, Mashhad, IranThe grain moisture content at harvest time is a key factor that limits harvesting windows. The present study aimed to develop a new methodology to predict wheat moisture content by using multi-layer perceptron (MLP) and support vector regression (SVR) techniques. Five input variables included the number of days after sowing, air temperature, air relative humidity, wind speed on an hourly basis, and precipitation on a 6-hour basis. The study area was Sari County located in the north of Iran. Data were collected from field experiments in two crop years (2016/17 and 2017/18). The results indicated that the developed MLP model outperformed the SVR model in determining wheat moisture content by R2 and RMSE value of 0.92 and 2.09% (wet basis) against 0.79 and 3.09%, respectively. In conclusion, the developed MLP model can be considered a useful method to estimate wheat moisture content at harvest time.http://www.sciencedirect.com/science/article/pii/S2214317319301696Artificial neural networkCerealHarvestModelSupport vector machine |
| spellingShingle | Shamsollah Abdollahpour Armaghan Kosari-Moghaddam Mohammad Bannayan Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques Information Processing in Agriculture Artificial neural network Cereal Harvest Model Support vector machine |
| title | Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques |
| title_full | Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques |
| title_fullStr | Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques |
| title_full_unstemmed | Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques |
| title_short | Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques |
| title_sort | prediction of wheat moisture content at harvest time through ann and svr modeling techniques |
| topic | Artificial neural network Cereal Harvest Model Support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S2214317319301696 |
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