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| Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR) |
| Ali Mohammadi Torkashvand1, Abbas Ahmadi2, Niloofar Layegh Nikravesh1 |
1 Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
2 Department of Soil Science, University of Tabriz, Tabriz 5166616471, Iran |
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Abstract Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit. In recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANNs) are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations (nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg)) alone (P1), combination of nutrients concentrations (P2), nutrient concentration ratios alone (P3), and combination of nutrient concentrations and nutrient concentration ratios (P4). The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets (P1, P2 and P4). However, the application of P3 (N/Ca ratio) as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error (RMSE) were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.
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Received: 25 June 2016
Accepted:
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Corresponding Authors:
Correspondence Ali Mohammadi Torkashvand, E-mail: m.torkashvand54@yahoo.com
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Cite this article:
Ali Mohammadi Torkashvand, Abbas Ahmadi, Niloofar Layegh Nikravesh .
2017.
Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture, 16(07): 1634-1644.
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