Agriculture Prediction Using ML

EasyChair Preprint 12548

Abstract

Agriculture is one of the most important and widely practised professions in India, and it has
contributed significantly to the growth of our nation. Around 60used for agriculture. Growing crop
output is seen as a crucial component of agriculture since it helps meet the demands of 1.2 billion
people. Using machine learning to solve practical and real-world crop productivity problems can be
challenging. Usually, if we own a piece of land, we need to have a basic knowledge of the kinds of
crops that ought to be grown there. Many aspects of the soil must be present for agriculture to thrive.
Crop production is a challenging undertaking because it requires taking into account a number of
factors, including temperature, soil type, humidity, and others. Making educated decisions about
storage and business will be significantly simpler for farmers and other stakeholders if it is simple
to locate the crop to be grown before seeding it. By monitoring agricultural regions based on soil
qualities and counselling farmers on the best crop, the proposed project will assist in the settlement
of agricultural difficulties by advising them on how to significantly increase production and decrease
loss. According to the description, this study is a recommendation system that uses a number of
machine learning techniques to suggest suitable crops based on input soil factors

Keyphrases: Challenges and Limitations, Refrences, abstract, future scope, literature review, methodology

@booklet{EasyChair:12548,
  author    = {Ronak Chavda and Borad Zenish and Aniket Parmar and Nihal Patel},
  title     = {Agriculture Prediction Using ML},
  howpublished = {EasyChair Preprint 12548},
  year      = {EasyChair, 2024}}