Comparative Analysis of Machine Learning Algorithms for Crop Recommendations

EasyChair Preprint no. 10597

Abstract

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

@Booklet{EasyChair:10597,
  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}}