Agroclimatology‐Based Yield Model for Carrot Using Multiple Linear Regression and Artificial Neural Networks

Abstract

Understanding the relationship between root bulking and agroclimatological factors can aid in predicting the yield and quality of processing carrot (Daucus carota L.). Field trials (four field seasons) with selected cultivars at various seeding rates, seeding dates, and harvest dates were conducted for three carrot types, viz., baby, diced, and sliced, and yield components were monitored. The corresponding weather data, such as minimum and maximum temperature, solar radiation, and rainfall, were recorded. Data from the 2006, 2007, and 2009 field seasons were used for model development, while 2008 data were reserved for the validation. Following a forward‐stepwise regression procedure to identify highly correlated input factors, feed‐forward back‐propagated artificial neural network (ANN) and multiple linear regression (MLR) models were developed. After validation, the best performing models were identified based on a ranking system that weighed the root mean square error (RMSE) and the fitness of the model (R2). For baby carrots, the Sugarsnax‐based MLR model exhibited 23% lower RMSE than the ANN for the desirable yield component. In diced carrots, predictions from both models (ANN and MLR) exhibited a good fit, with high R2 values (0.80–0.90). For sliced carrots, Topcut‐based ANN models predicted the majority of the yield components consistently better than MLR models. When MLR and ANN models were compared, their efficiencies differed with carrot type and yield component. The MLR models underperformed in modeling processes that were inherently nonlinear compared with ANN. Nonetheless, ANN models suffered from overfitting and consequently at times failed to demonstrate extrapolation capabilities.