Normal view MARC view ISBD view

Quality assessment of seed using supervised machine learning technique

By: Ramanath Kini, M. G.
Contributor(s): Bhandarkar, Rekha.
Publisher: USA Springer 2023Edition: Vol.104(4), Aug.Description: 901-909p.Subject(s): Humanities and Applied SciencesOnline resources: Click here In: Journal of the institution of engineers (India): Series BSummary: Agricultural seeds constitute the basic inputs and raw materials that lead to increased crop yields and sustained growth in agricultural production. They are a major source of protein and vitamins for human consumption. Wheat is one of the highest protein cereals. Seed quality plays a significant role in obtaining a good yield, but it is difficult to find out seed quality manually. To overcome this problem, Image Processing technology and Machine Learning techniques are used to classify seeds according to their quality. Images are analyzed for texture, morphology, and color of the grain. This paper presents a solution supporting quality analysis using Machine Learning. From the images of the seed, the considered attributes of the dataset are perimeter, area, diameter, and centroid. Also, the attributes of the texture dataset are contrast, correlation, energy, and homogeneity. Machine Learning is used to classify the seed quality by comparing each dataset (shape and texture) with the trained datasets.
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Call number Status Date due Barcode Item holds
Articles Abstract Database Articles Abstract Database School of Engineering & Technology
Archieval Section
Not for loan 2024-0316
Total holds: 0

Agricultural seeds constitute the basic inputs and raw materials that lead to increased crop yields and sustained growth in agricultural production. They are a major source of protein and vitamins for human consumption. Wheat is one of the highest protein cereals. Seed quality plays a significant role in obtaining a good yield, but it is difficult to find out seed quality manually. To overcome this problem, Image Processing technology and Machine Learning techniques are used to classify seeds according to their quality. Images are analyzed for texture, morphology, and color of the grain. This paper presents a solution supporting quality analysis using Machine Learning. From the images of the seed, the considered attributes of the dataset are perimeter, area, diameter, and centroid. Also, the attributes of the texture dataset are contrast, correlation, energy, and homogeneity. Machine Learning is used to classify the seed quality by comparing each dataset (shape and texture) with the trained datasets.

There are no comments for this item.

Log in to your account to post a comment.

Click on an image to view it in the image viewer

Unique Visitors hit counter Total Page Views free counter
Implemented and Maintained by AIKTC-KRRC (Central Library).
For any Suggestions/Query Contact to library or Email: librarian@aiktc.ac.in | Ph:+91 22 27481247
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha