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.Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database | School of Engineering & Technology Archieval Section | Not for loan | 2024-0316 |
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.
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