loading page

Prediction of Crop Yield Using Machine Learning Approaches for Agricultural Data
  • +2
  • Simanta Hazra ,
  • Sunil Karforma ,
  • Abhishek Bandyopadhyay ,
  • Sayantani Chakraborty ,
  • Debasis Chakraborty
Simanta Hazra
Asansol Engineering College

Corresponding Author:[email protected]

Author Profile
Sunil Karforma
Author Profile
Abhishek Bandyopadhyay
Author Profile
Sayantani Chakraborty
Author Profile
Debasis Chakraborty
Author Profile

Abstract

In this paper, two crop datasets are investigated.  First one is numerical data which is downloaded crop dataset from   https://github.com/Shrey-B/AV-Janatahack-Machine-Learning-in-Agriculture obtained from US field data collection. The dataset contains 88858 labeled samples, eight features and three classes.  Second one is a collection of crop image data.  This dataset is downloaded from https://www.kaggle.com/datasets/aman2000jaiswal/agriculture .  Crop image dataset contains 1005 samples having five type of images namely maize, wheat, jute, rice and sugarcane. Each sample crop image consists of 224 × 224 pixels) of all category.