A Hybrid Deep Learning Framework Approach for the Detection of Different Varieties of Grain Types

Nijhawan, Rahul; Ashish, Mritunjay; Ahuja, A. K.; Yadav, Naveen

doi:10.1109/smart52563.2021.9676325

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Main aim of this research paper is to help in the difficulty of vendors making fraudulent transportation in account of sales. The authors of [2] discussed A deep learning framework approach that can detect different varieties of grain types has been developed. This approach uses a hybrid of two different deep learning models, which improves the accuracy of grain type detection.…”

Section: IImentioning

confidence: 99%

Literature Survey on Grain Type Prediction Using Machine Learning

Reddy¹,

Gupta²,

Raj³

et al. 2023

IJRASET

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Classification of grains according to their type becomes very much necessary because there are multiple grains available in the market today. Classification of Grains according their types manually is not efficient. Classification becomes a really wearisome task when it comes to doing it hand-operated instead of automating the process. Manually classifying would be a grueling task. There is a need for mentally keen and quick system which can get better of this difficulty by automating these processes. It should be able to recognize and label the every single grain according to their respective type automatically. Therefore we conducted a literature survey on how various seeds were classified and various analysis which were used to obtain the various desired result. Here we observed that various researchers used k-means, CNN and various other algorithms.

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Section: IImentioning

confidence: 99%