RT Journal Article
T1 Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model
A1 Borrero Sánchez, Juan Diego
A1 Borrero Domínguez, Juan Diego
AB This study presents a novel hybrid model that combines two different algorithms to increasethe accuracy of short-term berry yield prediction using only previous yield data. The model integrates both autoregressive integrated moving average (ARIMA) with Kalman filter refinement and neural network techniques, specifically support vector regression (SVR), and nonlinear autoregressive (NAR) neural networks, to improve prediction accuracy by correcting the errors generated by the system. In order to enhance the prediction performance of the ARIMA model, an innovative method is introduced that reduces randomness and incorporates only observed variables and system errors into the state-space system. The results indicate that the proposed hybrid models exhibit greater accuracy in predicting weekly production, with a goodness-of-fit value above 0.95 and lower root mean square error (RMSE) and mean absolute error (MAE) values compared with non-hybrid models. The study highlights several implications, including the potential for small growers to use digital strategies that offer crop forecasts to increase sales and promote loyalty in relationships with large food retail chains. Additionally, accurate yield forecasting can help berry growers plan their production schedules and optimize resource use, leading to increased efficiency and profitability. The proposed model may serve as a valuable information source for European food retailers, enabling growers to form strategic alliances with their customers.
PB MDPI
SN 2311-7524 (electrónico)
YR 2023
FD 2023-05
LK https://hdl.handle.net/10272/22042
UL https://hdl.handle.net/10272/22042
LA eng
NO Borrero, J. D., & Borrero-Domínguez, J.-D. (2023). Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model. In Horticulturae (Vol. 9, Issue 5, p. 549). MDPI AG. https://doi.org/10.3390/horticulturae9050549
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026