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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44965


Ms R Divya, Dr C V Pavithra, Ms A Jeyashree, Ms K Bavithra


The agricultural land is decreasing day by day choosing a particular crop for an area and optimal use of water is highly demanded in the agricultural sector to get more yield and to minimize fertilizer cost. The goal of this project is to accurately predict the crop for the particular area and to water the crops based on the climatic conditions. Elaborating a functional and efficient prediction system is a very complex task due to the high number of factors that the technician considers when managing prediction in an optimal way. Automatic learning systems propose an alternative to traditional ways by means of the automatic elaboration of predictions based on the learning of an agronomist. The aim of this project is the study of several learning techniques in order to determine the goodness and error relative to expert decision. LightGBM, XGBoost and CatBoost as engines of the prediction. Certain parameters are considered and evaluated like pH of the soil, temperature, the amount of water required, humidity, and rainfall are obtained and these data are processed using machine learning concepts develop prediction systems and parameter like humidity, rainfall and soil moisture are used to develop irrigation management systems.


Light GBM, XG Boost, Cat Boost

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