Weather forecasting can help with a farmer’s day-to-day business decisions. These decisions include crop irrigation, time to fertilize, and what days are suitable for working in the field.
To produce a successful crop, a farmer needs to be aware of the moisture, light, and temperature. Detailed weather information, which includes past records, present weather and future forecasts are required. Rain is usually seen as a benefit to crops and fields, but there is an “ideal” amount of rainfall in any given growing season for most crops.
Several methods are available for rainfall forecasting, such as numerical weather prediction (NWP) models, statistical methods, and machine learning techniques. Among these, machine learning techniques, such as artificial neural network (ANN), k-nearest neighbor and random forest model, are more suitable for rainfall forecasting because physical processes affecting rainfall occurrence are highly complex and non-linear.
Therefore, in order to produce efficient accurate results of forecasting rainfall, we have developed a way that can help farmers in their decisions, based on the long term predicted rainfall model, farmers would be able to come up with when to plant and when to use the available water in the farm to water the crop until rain comes!