A machine learning based crop recommendation system and user-friendly android application for cultivation. (13th March 2023)

• Record Type: Journal Article
• Authors: Tonni, Kaniz Fatima; Chowdhury, Mahfuzulhoq
• Is Part Of: International journal of smart technology and learning. Volume 3:Number 2(2023)
• Page Start: 168
• Page End: 186
• Publication Date: 2023-03-13
• Subjects: crop recommendation -- machine learning -- prediction -- evaluation -- cultivation -- Bangladesh -- Android application; 371.33405
• Languages: English
• ISSNs: 2056-4058

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.