Analysis and prediction of seed quality using machine learning

Title

Analysis and prediction of seed quality using machine learning

Creator

Srinivasaiah R.; Meenakshi; Channegowda R.H.; Jankatti S.K.

Description

The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithms predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the projects primary goal is to develop the best method for the more accurate prediction of seed quality. 2023 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-13, No. 5, pp. 5770-5781.

Date

2023-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Agriculture; Classification; Convolution neural network; Prediction; Seed quality

Coverage

Srinivasaiah R., Department of Computer Science and Engineering, CHRIST Deemed to be University, Bengaluru, India; Meenakshi, Department of Computer Science and Engineering, RNS Institute of Technology, Bengaluru, India; Channegowda R.H., Department of Electronics and Communication, Dayananda Sagar Academy of Technology and Management, Bengaluru, India; Jankatti S.K., Department of Computer Science and Engineering, Dayananda Sagar University, Bengaluru, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v13i5.pp5770-5781

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