A machine learning based crop recommendation system and user-friendly android application for cultivation Online publication date: Fri, 17-Mar-2023
by Kaniz Fatima Tonni; Mahfuzulhoq Chowdhury
International Journal of Smart Technology and Learning (IJSMARTTL), Vol. 3, No. 2, 2023
Abstract: Bangladesh is essentially an agricultural nation, and its economy is heavily dependent on it. A farmer could plant a crop if he knew which one would yield more. The existing literature works fail to provide a user-friendly mobile application for cultivation as well as machine learning-based crop recommendation by taking different factors into account. This paper creates a mobile application that enables farmers to forecast viable crops based on climate factors like humidity, rainfall, and temperature as well as soil characteristics. The suggested model is used to forecast agricultural production using crop records of diverse crops with various properties of soil and climate parameters. The suggested model offers farmers a comprehensive list of recommendations to help them choose crops that are best for them based on particular considerations like production costs and fertiliser recommendations. The user's feedback shows satisfactory remarks in terms of its usefulness.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Smart Technology and Learning (IJSMARTTL):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook