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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    YIELD PREDICTION IN CROP WITH RAINFALL, PESTICIDE AND TEMPERATURE ANALYSIS WITH KNN OPTIMIZED WITH GRID SEARCH CV
    M.Revathi, Dr. N. Kanya, Dr. Victo Sudha George
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1339-1357 . 

    Abstract

    For agricultural planning reasons, an essential problem is the precise calculation of yield for all the crops included. Machine learning (ML) is a crucial strategy for finding workable and efficient answers to this issue. Crop yield prediction has emerged as an intriguing subject of study due to recent advances in information technology for the agricultural sector. One of the most pressing issues with the current data set is yield prediction. There are better options available for this purpose, such as data mining and machine learning. In agriculture, many machine-learning approaches are used and assessed for forecasting crop output for the next year. A method to forecast agricultural production using historical data is presented and implemented in this work. To do this, we provide a revamped KNN model that includes hyper parameter tuning and is optimized using Grid search CV. Additionally, the outcomes are evaluated in comparison to several machine learning techniques such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). A higher adjusted R2 score of 0.961 indicates that the suggested model has a better ability to forecast agricultural data output.

    Keyword

    Yield prediction, Decision tree, Random forest, support vector machine, Optimized KNN.


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ISSN 1004-9037

         

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