Abstract
Crop prediction is the art of forecasting. Based on previous experiences the farmers are used to predicting the crop. In past decades to overcome this traditional method, various ML algorithms, and other mining techniques are used on agricultural data. This proposed system is implemented by using Decision tree and Random Forest algorithms along with remote sensing techniques to predict the crop. Global measurements of soil moisture and atmospheric moisture are provided through remote sensing data, such as those from the earth satellite program Soil Moisture Active Passive (SMAP). For a straightforward graphical depiction, the Normalized Difference Vegetation Index (NDVI) dataset was employed. The Plants Condition Index (VCI) and Temperature Condition Index (TCI) are used to map and monitor the health and production of vegetation. For the estimation and forecast of the crop, this application system needs resources like NDVI data, soil moisture, surface temperature, and rainfall data.
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