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Comparative Analysis of Machine Learning Algorithms for Crop Recommendations

EasyChair Preprint no. 10597

4 pagesDate: July 19, 2023


India's economy heavily relies on agriculture, which serves as a crucial industry for its growth and survival. The country is recognized as one of the largest producers of various agricultural products. Soil, an essential component of crop cultivation and non-renewable natural resource, plays a pivotal role in sustaining life. Traditionally, farmers utilized their experience to select the most appropriate crops based on soil characteristics. However, the current agricultural landscape calls for a recommendation system utilizing machine learning algorithms to determine the ideal crop for specific type of soil. Crop recommendation systems have gained significant attention in the field of agriculture, aiding farmers in making informed decisions about suitable crops for their specific conditions.

Keyphrases: Gradient Boosting, KNN Decision Tree, machine learning, Naive Bayes, Random Forest

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Chinmay Vyapari and Prathamesh Bhosale and Ameya Parkar},
  title = {Comparative Analysis of Machine Learning Algorithms for Crop Recommendations},
  howpublished = {EasyChair Preprint no. 10597},

  year = {EasyChair, 2023}}
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