Crop Selection According to Environment Using Latest Technique of Machine Learning

EasyChair Preprint 10175

Abstract

The study of agriculture is gaining popularity. In agriculture, crop prediction is very important because soil factors like temperature, humidity, and rainfall have a big effect. Ranchers used to have the option to pick the yield they needed to plant, screen its encouraging, and choose when to reap it. Notwithstanding, the quick changes in the climate have made it unthinkable for farmers to do as such. Thusly, lately, machine learning calculations play assumed the part of forecast, and in this review, farming creation was determined utilizing different these methodologies. To guarantee the accuracy of a particular machine learning (ML) model, effective feature selection methods must be used to preprocess raw data into a dataset suitable for machine learning. Only data characteristics that have a significant impact on the model's output should be included in order to reduce duplication and improve model quality. The model only has the most important features because the best feature selection was made. If every characteristic from the raw data is joined without their value during the model-building process, our model will be too complicated. In addition, the model's output accuracy would be decreased if factors that have little effect on the model were included.

Keyphrases: Agriculture, Crop forecast, feature selection

@booklet{EasyChair:10175,
  author    = {Rajkumar Yathirajyam and Mohit Malik and Manish Kumar and Divyam Sharyan and Vipul Nuthalapati and Pravinkumar Tiwari},
  title     = {Crop Selection According to Environment Using Latest Technique of Machine Learning},
  howpublished = {EasyChair Preprint 10175},
  year      = {EasyChair, 2023}}