Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS)
Suitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most commo...
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| Format: | Article |
| Language: | English |
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Ferdowsi University of Mashhad
2014-09-01
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| Series: | Journal of Agricultural Machinery |
| Subjects: | |
| Online Access: | https://jame.um.ac.ir/index.php/jame/article/view/40425 |
| _version_ | 1818618964196982784 |
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| author | R Sedghi Y Abbaspour Gilandeh |
| author_facet | R Sedghi Y Abbaspour Gilandeh |
| author_sort | R Sedghi |
| collection | DOAJ |
| description | Suitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. In this paper, a method based on adaptive neuro fuzzy inference system (ANFIS) was used to describe the soil fragmentation for seedbed preparation with combination of primary and secondary tillage implements including subsoiler, moldboard plow and disk harrow. Adaptive neuro fuzzy inference system (ANFIS) is a suitable approach to solving non-linear problems. ANFIS is a combination of fuzzy inference system (FIS) and an artificial neural network (ANN) method and it uses the ability of both models. In this study, the model inputs included “soil moisture content”, “tractor forward speed”and “working depth”. The performance of the model was evaluated using the statistical parameters of root mean square error (RMSE), percentage of relative error (ε), mean absolute error (MAE) and the coefficient of determination (R2). These parameters were determined as 0.135, 3.6%, 0.122 and 0.981, respectively. For the evaluation of the ANFIS model, the predicted data using this model were compared to the data of artificial neural network model. The simulation results by ANFIS model showed to be closer to the actual data compared with those made by the artificial neural network model. |
| first_indexed | 2024-12-16T17:29:57Z |
| format | Article |
| id | doaj.art-4d38c303507248938b7e2644c44d47e9 |
| institution | Directory Open Access Journal |
| issn | 2228-6829 2423-3943 |
| language | English |
| last_indexed | 2024-12-16T17:29:57Z |
| publishDate | 2014-09-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | Journal of Agricultural Machinery |
| spelling | doaj.art-4d38c303507248938b7e2644c44d47e92022-12-21T22:22:57ZengFerdowsi University of MashhadJournal of Agricultural Machinery2228-68292423-39432014-09-014238739810.22067/jam.v4i2.404258308Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS)R SedghiY Abbaspour GilandehSuitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. In this paper, a method based on adaptive neuro fuzzy inference system (ANFIS) was used to describe the soil fragmentation for seedbed preparation with combination of primary and secondary tillage implements including subsoiler, moldboard plow and disk harrow. Adaptive neuro fuzzy inference system (ANFIS) is a suitable approach to solving non-linear problems. ANFIS is a combination of fuzzy inference system (FIS) and an artificial neural network (ANN) method and it uses the ability of both models. In this study, the model inputs included “soil moisture content”, “tractor forward speed”and “working depth”. The performance of the model was evaluated using the statistical parameters of root mean square error (RMSE), percentage of relative error (ε), mean absolute error (MAE) and the coefficient of determination (R2). These parameters were determined as 0.135, 3.6%, 0.122 and 0.981, respectively. For the evaluation of the ANFIS model, the predicted data using this model were compared to the data of artificial neural network model. The simulation results by ANFIS model showed to be closer to the actual data compared with those made by the artificial neural network model.https://jame.um.ac.ir/index.php/jame/article/view/40425TillageSoil fragmentationMedian weight diameter (MWD)Adaptive neuro fuzzy inference systemArtificial neural network. |
| spellingShingle | R Sedghi Y Abbaspour Gilandeh Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS) Journal of Agricultural Machinery Tillage Soil fragmentation Median weight diameter (MWD) Adaptive neuro fuzzy inference system Artificial neural network. |
| title | Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS) |
| title_full | Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS) |
| title_fullStr | Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS) |
| title_full_unstemmed | Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS) |
| title_short | Prediction of Soil Fragmentation During Tillage Operation Using Adaptive Neuro Fuzzy Inference System (ANFIS) |
| title_sort | prediction of soil fragmentation during tillage operation using adaptive neuro fuzzy inference system anfis |
| topic | Tillage Soil fragmentation Median weight diameter (MWD) Adaptive neuro fuzzy inference system Artificial neural network. |
| url | https://jame.um.ac.ir/index.php/jame/article/view/40425 |
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