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

Corresponding Author:[email protected]

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Sunil Karforma
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Abhishek Bandyopadhyay
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Sayantani Chakraborty
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Debasis Chakraborty
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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.