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• W4378977319 abstract "Agriculture is a fundamental and basic source of living of many people and it is considered as a main part in economy and finance of any country. It is highly dependent on climate, weather and environment factors. Efficiency in crop productivity depends on agricultural elements embracing soil, water, temperature, and climate changes. The challenges in agriculture field, specifically crop prediction are complex and a machine-learning approach for crop cultivation is proposed for the achievement of production goals. This paper is proposed to design a machine learning-based system for crop selection. It can analyze the quality of soil and water, agro-climatic conditions, and also the necessary crop requirements for selecting suitable crops using the database. Also, supervising crop growth is proposed for monitoring the proper growth of the crop. A machine learning technique to develop crop prediction and soil quality check models. This model works on Machine Learning Techniques like Support Vector Machine (SVM) algorithm. The classifiers like Random Forest (RF), Gaussian Naive Baye's (NB), and k Nearest Neighbor (kNN) are used in crop prediction process. Based on machine learning algorithm and prediction accuracy, this model can help farmers at different locations for crop cultivation management." @default.
• W4378977319 created "2023-06-02" @default.
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• W4378977319 date "2023-04-26" @default.
• W4378977319 modified "2023-10-17" @default.
• W4378977319 title "Analysis of Machine Learning Technique for Crop Selection and Prediction of Crop Cultivation" @default.
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• W4378977319 doi "https://doi.org/10.1109/icict57646.2023.10134371" @default.
• W4378977319 hasPublicationYear "2023" @default.
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