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

Abstract

AbstractThere 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 R2and 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.