Yield prediction of apricot using a hybrid particle swarm optimization-imperialist competitive algorithm- support vector regression (PSO-ICA-SVR) method - Esfandiarpour-Boroujeni, I.; Karimi, E.; Shirani, H.; Esmaeilizadeh, M.; Mosleh, Z. (Scientia horticulturae, 2019)
•The potential use of a hybrid PSO-ICA-SVR algorithm to predict apricot yield was evaluated.•The hybrid algorithm identified 18 variables out of 61 studied variables as the most effective variables.•Irrigation frequency in the growing season was the highest importance for prediction of the apricot yield.•The hybrid algorithm had a high efficiency for prediction of apricot yield. Determining the important factors that affect the crop yield would be helpful for optimization of irrigation water usage, fertilizer applications, and other inputs and resources in farms. The main objective of this study was to evaluate the performance of a hybrid particle swarm optimization-imperialist competitive algorithm-support vector regression (PSO-ICA-SVR) method to predict apricot yield and to identify important factors affecting its yield in Abarkuh region, Yazd Province, Iran. One hundred ten apricot orchards were selected randomly and soil samples were taken at two layers (0–40 and 40–80 cm). Besides, water samples and leaves from branches without fruit were taken in each sampling point. Management information and apricot yields were achieved by completing a questionnaire. After performing yield modeling in MATLAB software, the results showed that the application of this hybrid algorithm identified 18 variables out of 61 studied variables as the most effective variables in apricot yield. The order of importance of selected variables in relation to the sensitivity analysis was irrigation intervals, spacing of rows planting, spacing of trees on each row, magnesium and nitrogen content of leaf, cultivar type, sand percentage and salinity of first studied layer, leaf sodium, soluble magnesium of first studied layer, sand percentage of second studied layer, age of trees, available potassium of first studied layer, silt percentage of second studied layer, coarse fragments percentage of first studied layer, soluble calcium, soluble magnesium and salinity of second studied layer. The validation results showed that the hybrid algorithm was able to estimate apricot yield with a relatively high accuracy (RMSE = 1.737 for training data and RMSE = 2.329 for testing data). Also, the use of efficiency index (EFI) showed that the hybrid algorithm has a high efficiency (about 99%) for prediction of apricot yield. As the apricot yield has a high sensitivity to the irrigation frequency, adopting management practices should be consider to increase water use efficiency in arid and semi-arid regions.