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- W4377700179 abstract "India’s economy relies primarily on agriculture which also serves as a source of survival for 60% to 70% of the nation’s people. Because of environmental influences like soil quality, weather, heavy rain, drought, seed damage, fertilizer, and pesticide, farmers are not acquiring the proper yield and profit and are therefore unable to produce a large amount of food. The identified challenge can be overcome by deploying the Random Forest Algorithm to suggest the crop that is most suited for the soil based on considerations namely potassium (K), nitrogen (N), phosphorous (P), pH, temperature, humidity & rainfall. The main objective is to develop a model that suggests a crop that is ideal for a soil, based on the Kaggle dataset trained and to illustrate that the Random Forest Algorithm has demonstrated superior accuracy when compared. The resultant model, that incorporates the Random Forest Algorithm, recommends the optimal crop based on the soil’s condition when inputs are acquired via the web interface. The dataset contains data on the levels of potassium (K), nitrogen (N), phosphorous (P), pH, temperature, humidity & rainfall." @default.
- W4377700179 created "2023-05-24" @default.
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- W4377700179 date "2023-04-07" @default.
- W4377700179 modified "2023-09-25" @default.
- W4377700179 title "Crop Recommender Framework Utilizing ML Approach" @default.
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- W4377700179 doi "https://doi.org/10.1109/i2ct57861.2023.10126287" @default.
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