International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Advanced Crop Recommendation Systems: Leveraging Random Forest and KNN Algorithms

Author(s) Burla Uday Theja, Rajan Kakkar, Sanjay Palegar, Chirumamilla Sriram, Ravi Gowtham Mutyala, Ashhar Alam
Country India
Abstract In the time of precision agriculture, crop selection optimization is crucial to maximizing productivity and resource efficiency. This article explores the combination of Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms to enhance crop recommendation systems. Crop performance and environmental characteristics have complex and non-linear connections that are captured by the reliable and accurate RF technique. Using a sizable dataset that includes crop yields, climate factors, and soil properties, the study assesses the efficacy of the integrated system in comparison to traditional recommendation methodologies. Preliminary we implemented KNN first and got 96% accuracy and then implemented on RF to get 99% accuracy and also created a GUI using tkinter to predict crops on random value.
Keywords Crop Recommendation, Precision Agriculture, Random Forest, K-Nearest Neighbors (KNN), Machine Learning, Data Mining, Agricultural Optimization, Yield Prediction, Environmental Factors, Soil Properties, Climate Conditions, Classification Algorithms, Predictive Modeling, Crop Performance, Data Analytics, Decision Support Systems, Sustainable Agriculture, Hybrid Models, Farming Technology, Computational Agriculture
Field Engineering
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-08-04
Cite This Advanced Crop Recommendation Systems: Leveraging Random Forest and KNN Algorithms - Burla Uday Theja, Rajan Kakkar, Sanjay Palegar, Chirumamilla Sriram, Ravi Gowtham Mutyala, Ashhar Alam - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.25075
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.25075
Short DOI https://doi.org/gt556h
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