Abstract:
AquaCrop, a leading crop simulation model, improves agricultural practices by simulating an
extensive tapestry of diverse crop conditions. Nonetheless, the model's strong demand for inputs,
which includes climate, agricultural, and management data, causes issues in many developing
nations. As a result, the development of an exceptionally accurate model with minimum essential
inputs becomes an absolute need. The purpose of this study was to (a) deploy the AquaCrop model
to simulate sugarcane yield under various weather scenarios in Sri Lanka's dry zone, and (b)
create and evaluate the performance accuracy of an intelligent model for predicting sugarcane
yield using climatological inputs using Machine Learning (ML) algorithms. Observational datasets
comprising four growth seasons were used to calibrate and validate AquaCrop default
parameters for sugarcane farming in the research location. At the Sugarcane Research Institute in
Udawalawa, one hundred scenarios were simulated on a field plot, and the AquaCrop model
provided the corresponding yield values. For the prediction model, this study used three ML
algorithms: Random Forest (RF), Support Vector Regression, and Gradient Boosting Regressor
(GBR). These algorithms were tested in a variety of scenarios, using input variables such as
minimum and maximum temperatures, sunshine hours, average relative humidity, rainfall, and
wind velocity. The results revealed that the AquaCrop model was successfully validated in the
specified study area, with an R2 of 0.86. In the comparison of predicted and observed values, the
GBR had an R2 of 0.79 and an RMSE of 3.19 among the ML models. Similarly, the RF model
generated an R2 score of 0.74, indicating a good relationship between the projected and actual
sugarcane yield data. As a result, it is evident that the GBR and RF algorithms are the best ML
models for predicting the yield of sugarcane based on the research location's specific
climatological variables. Additional research, extensive model validations, and integration into
real agricultural systems are required to assure the effective use and acceptance of these models
by sugarcane growers.
