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Harnessing IoT and machine learning for sustainable agriculture: Predictive crop yield modeling in smart farming

Rashmi Gera, Anupriya Jain

Abstract


The integration of Internet of Things (IoT) technology in agriculture has transformed traditional farming methods, enabling the emergence of smart agriculture systems. This research paper focuses on utilizing IoT and machine learning techniques to forecast crop yields based on climatic and soil conditions. The study utilizes a dataset sourced from Kaggle, which includes information on 22 distinct crops, such as Maize, Wheat, Mango, Watermelon, and others. The dataset encompasses crucial climatic factors like temperature, humidity, and rainfall, as well as essential soil conditions necessary for optimal crop growth. By employing advanced machine learning algorithms on this dataset, the objective is to develop accurate models capable of predicting crop yields. This, in turn, assists farmers in making well-informed decisions regarding crop management and optimizing agricultural productivity. The outcomes of this research have significant implications for the agricultural industry, providing valuable insights into crop yield estimation and supporting the implementation of sustainable farming practices.


Keywords


IoT; machine learning; sustainable agriculture; smart farming; predictive modeling; crop yield estimation

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References


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DOI: https://doi.org/10.32629/jai.v7i4.1108

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Copyright (c) 2024 Rashmi Gera, Anupriya Jain

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