Crop Recommendation System using hybrid of KNN and Random Forest Classifier

Aruna Cathciyal G.; Viji D.; Sri Amirtha; P. Gajalakshmi

doi:10.36948/ijfmr.2023.v05i02.1666

Crop Recommendation System using hybrid of KNN and Random Forest Classifier

Author(s)	Aruna Cathciyal G., Viji D., Sri Amirtha, P. Gajalakshmi
Country	India
Abstract	Machine learning and its rapid advancement have significantly improved the way we interact with computers. We can find applications of machine learning in almost every field, like the IT industry, medicine, agriculture, etc. The idea of imparting machine learning to agriculture rose decades ago, and, as a result, many improvements were made in the field of agriculture. Various models are developed to predict the crop and yield using machine learning algorithms like decision trees, but the main problem with using algorithms like decision trees is that they do not provide the desired accuracy, which may lead to incorrect predictions. This paper proposes a user-friendly crop recommendation and yield prediction system. The user provides the following as input: state name, district name, soil type, and season. To recommend the crop and predict the yield of the crop, a combination of K-nearest neighbor (KNN) and random forest (RM) is used. The K-nearest neighbor algorithm showed 98% accuracy, and the Random Forest algorithm showed 96% accuracy
Keywords	Crop recommendation, yield prediction, Machine learning, KNN, Random Forest
Field	Biology > Agriculture / Botany
Published In	Volume 5, Issue 2, March-April 2023
Published On	2023-03-08
Cite This	Crop Recommendation System using hybrid of KNN and Random Forest Classifier - Aruna Cathciyal G., Viji D., Sri Amirtha, P. Gajalakshmi - IJFMR Volume 5, Issue 2, March-April 2023. DOI 10.36948/ijfmr.2023.v05i02.1666
DOI	https://doi.org/10.36948/ijfmr.2023.v05i02.1666
Short DOI	https://doi.org/grwstg

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Crop Recommendation System using hybrid of KNN and Random Forest Classifier

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