Crop Prediction Using Machine Learning and Artificial Neural Network
- DOI
- 10.2991/978-94-6463-196-8_43How to use a DOI?
- Keywords
- Agricultural Sector; Crop Prediction; Deep Learning; Machine Learning
- Abstract
Agriculture Sector being most vital parts of every Nation. The production of a crop mostly relies on numerous features and result is relied on the end yield and on the selling rate of that crop. In today’s world there is a growth of countless technologies which can predict the growth of a crop on a regular basis, what must be added in that type of soil to make it more productive on-the-basis of study of that region. Crop Prediction using Deep Learning methods is indeed an upcoming challenge in the field of Agriculture.
Deep Learning would increase the efficiency of the workforce, a huge amount of time would be spending in learning analytics, therefore increasing one’s concentration leading to predicative analytics also there would be personalized learning, the dependency on others would slowly start to terminate. The main aim of this paper is to focus on crop prediction by using numerous algorithms of machine as well as deep learning, and then to draw a comparison on the results and other performance measure of the different algorithms of Machine Learning and Deep Learning.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Tanya Saraswat PY - 2023 DA - 2023/08/10 TI - Crop Prediction Using Machine Learning and Artificial Neural Network BT - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022) PB - Atlantis Press SP - 561 EP - 568 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-196-8_43 DO - 10.2991/978-94-6463-196-8_43 ID - Saraswat2023 ER -