Machine Learning  Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka

Table 7

Crop-weather models.

Ref.	Crop	Country	Evaluation criteria	Weather indices	The most influential weather indices
[6]	Paddy	India	Full model and stepwise MLR	Rice area, number of days with minimum temperature below 22°C, average daily temperature (maximum and minimum), sunshine hours, rainfall, and solar radiation	Solar radiation
[21]	Corn	USA	Kincer’s method	Precipitation, temperature, sunshine, and relative humidity	Relative humidity
[22]	Crops	Uganda	MLR	Precipitation, temperature, and CO2 emissions	CO2 emissions
[23]	7 crops including paddy and corn	Taiwan	MLR	Temperature and precipitation	Temperature and precipitation
[24]	Paddy	India	Gaussian process regression (GPR) and lasso regression	Temperature, average humidity, rainfall, wind speed, UV index, sun hours, and pressure values	Rainfall
[25]	Wheat	China	RF, SVM, and GPR	Maximum temperature, minimum temperature, drought index, and precipitation	Minimum temperature
[26]	Paddy	Korea	Random forest	Temperature (minimum, mean, and maximum) and sunshine hours	Minimum temperature and sunshine hours