Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables.

ABSTRACT: Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced from the Kaggle repository to generate practical recommendations for crop selection or determination of required nutrient(s) in a given site. The datasets contain information on NPK, soil pH, and three climatic variables: temperature, rainfall, and humidity. The models namely Support vector machine, XGBoost, Random forest, KNN, and Decision Tree were trained using yields of individual data sets of 11 agricultural and 10 horticultural crops, as well as comb

SUBMITTER: Dey B 

PROVIDER: S-EPMC10844259 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature