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Estimation of kiwifruit yield by leaf nutrients concentration and artificial neural network

Published online by Cambridge University Press:  08 June 2020

Ali Mohammadi Torkashvand ,
Afsoon Ahmadipour  and
Amin Mousavi Khaneghah Open the ORCID record for Amin Mousavi Khaneghah [Opens in a new window]
Ali Mohammadi Torkashvand*
Affiliation:
Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
Afsoon Ahmadipour
Affiliation:
Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
Amin Mousavi Khaneghah
Affiliation:
Department of Food Science, Faculty of Food Engineering, State University of Campinas (UNICAMP), Rua Monteiro Lobato, 80. Caixa Postal: 6121, CEP: 13083-862 Campinas, São Paulo, Brazil
*
Author for correspondence: Ali Mohammadi Torkashvand, E-mail: [email protected]; Amin Mousavi Khaneghah, E-mail: [email protected]
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Abstract

There is a fundamental concern regarding the prediction of kiwifruit yield based on the concentration of nutrients in the leaf (2–3 months before fruits harvesting). For this purpose, the current study was designed to employ an artificial neural network (ANN) to evaluate the kiwi yield of Hayward cultivar. In this regard, 31 kiwi orchards (6–7 years old) in different parts of Rudsar, Guilan Province, Iran, with 101 plots (three trees in every plot) were selected. The complete leaves of branches with fruits were harvested, and the concentration of nitrogen, potassium, calcium, and magnesium measured. After fruit harvesting in late November, the fruit yield of each plot was evaluated along with the fresh and dry weights of the fruit. The ANN analyses were carried out using a multi-layer perceptron with the Langburge-Marquardt training algorithm. Using calcium (Ca) as input data (Ca-model) was more accurate than using nitrogen (N-model). The maximum R2 and the lowest root mean square error was obtained when all nutrients and related ratios were considered as input variables. Since the difference between the proposed model and the model fitted by the calcium variable (Ca-model) was only about 6%, the Ca-model is recommended.

Keywords

Fruitmodellingmultilayer perceptronnutrientsyield
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 158 , Issue 3 , April 2020 , pp. 185 - 193
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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