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

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.

Using artificial neural network in predicting the key fruit quality of loquat

“…In this regard, Ca affects pollination and fruit formation in fruit trees (Freitas & Mitcham, 2012). Torkashvand (Torkashvand et al., 2020) showed a significant correlation between Ca concentration in leaves and fruit yield. Egilla (Egilla et al., 2005) assumed that the increase of potassium would lead to the increase of photosynthesis, and the yield and dry matter content will increase accordingly, and a lack of Fe content leads to a significant reduction in the fresh weight and number of fruit per tree (Álvarez‐Fernández et al., 2003).…”

“…The origin and variety of loquat can be analyzed qualitatively by combining near infrared and artificial neural network model (Fu et al., 2007), and the extraction rate of ursolic acid from loquat leaves was optimized based on neural network model (Sun et al., 2010). Meanwhile, many previous studies (Gholipoor & Nadali, 2019; Niedbała, 2019; Torkashvand et al., 2017, 2019) indicated that the artificial neural network model is a very effective and reliable forecasting tool, which has been widely used in agriculture and has a very high prediction accuracy.…”

Fruit quality prediction based on soil mineral element content in peach orchard

“…The relationship between fruit quality indexes and orchard soil mineral elements was complex and cannot be accurately revealed using conventional modeling techniques or mathematical methods. In recent years, an increasing number of researchers have used the ANN model as a forecasting tool for a variety of subjects, including agricultural research (Abdipour et al., 2019 ; Huang et al., 2021 ; Mazen et al., 2019 ; Torkashvand et al., 2019 ; Zhang et al., 2020 ), indicating that the ANN model was a highly effective forecasting tool. In essence, the neural network realized a mapping function from input to output.…”

Design and development of resistance heating apparatus‐cum‐solar drying system for enhancing fish drying rate

“…Artificial neural network becomes a proficient modeling implement including various benefits across different well‐known modeling methods, namely response surface methodology. Artificial neural network controls various variables to model for multi‐component nonlinear as well as linear regression issues (Srikanth et al, 2020: Srinivas et al, 2020: Torkashvand, Ahmadipour, & Khaneghah, 2020). Therefore, a multilayer feed‐forward back‐propagation neural network over one intake level, one concealed level, and one exit level topology was chosen in the current investigation in order to anticipate the moisture content and drying characteristics.…”

“…The statistical evaluation and ANOVA determine the confidence level by 0.01, 1, and 5% over single, mixed as well as square terms.2.12 | Artificial neural networkArtificial neural network becomes a proficient modeling implement including various benefits across different well-known modeling methods, namely response surface methodology. Artificial neural network controls various variables to model for multi-component nonlinear as well as linear regression issues(Srikanth et al, 2020: Srinivas et al, 2020: Torkashvand, Ahmadipour, & Khaneghah, 2020.Therefore, a multilayer feed-forward back-propagation neural network over one intake level, one concealed level, and one exit level topology was chosen in the current investigation in order to anticipate the moisture content and drying characteristics. ANN model consisted of four neurons, such as voltage, salt concentration, solar drying temperature, and loading density, to the input level, four neurons for the hidden level, and two neurons (final moisture content and overall drying rate) for the output level.…”