Crop Yield Prediction based on Indian Agriculture using Machine Learning - N BanuPriya, D Tejasvi , P Vaishnavi (International Journal of Modern Agriculture, 2021)
In our analysis, that we tend to have discovered withinside the previous studies papers is that everybody makes use of environmental condition factors like rain, daylight and agricultural factors like soil sort, nutrient possessed via the soil (Nitrogen, Potassium, etc.) however the matter is we want to gather the data so a third party will do this prediction and later it is explained to the farmer and this takes a variety of attempts for the farmer and he doesn’t perceive the technological study behind these factors. To make it straightforward and which can be directly utilized by the farmer this paper uses easy factors like the state and district is the farmer from, the crop and in what season (as in Kharif, Rabi, etc.). In India, there are more than a hundred vegetation plants across the entire country. These vegetation are categorized for better understanding and image. The data for this analysis has been nonheritable from the Indian Government Repository [1]. The statistics includes attributes like– State, District, Crop, Season, Year, Space and Production with around 2.5 Lakh observations. We used advanced regression techniques – Random Forest, Gradient Boost and Decision Tree to predict the yield and used Ensemble algorithms to minimize the error and reap higher predictions.