International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

IoT Based Crop Prediction using Machine Learning

Country India
Abstract To enhance agricultural productivity and resource allocation. Leveraging historical data encompassing various agricultural factors such as soil composition, weather conditions, and crop types, a predictive model is developed to forecast crop yields. Machine learning techniques, including regression and classification algorithms, are employed to analyze the intricate relationships within the dataset and predict the most likely outcomes for specific crops in different regions. The model is trained on a diverse dataset, considering variations in climate and soil characteristics, ensuring robustness and adaptability across different agricultural environments . The proposed crop prediction system not only provides accurate yield forecasts but also assists farmers in making informed decisions regarding crop selection and resource optimization. By harnessing the power of machine learning, this research contributes to sustainable agriculture by promoting precision farming practices. The integration of technology in crop prediction not only improves yield predictions but also supports the overall resilience of agricultural systems, fostering a data-driven approach that aligns with the evolving needs of the agricultural sector in a rapidly changing world.
Keywords IoT, machine learning, real-time data
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-02
Cite This IoT Based Crop Prediction using Machine Learning - ASWATHY M, ATHULYA ANIL, ABHIJITH B, ANANTHU RAJ, NIFSA NAZAR - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.13234
Short DOI

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