Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Quality Assessment of Seed Using Supervised Machine Learning Technique
https://orcid.org/http://orcid.org/0000-0002-2799-5875
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.
Quality Assessment of Seed Using Supervised Machine Learning Technique
https://orcid.org/http://orcid.org/0000-0002-2799-5875
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.
Quality Assessment of Seed Using Supervised Machine Learning Technique
https://orcid.org/http://orcid.org/0000-0002-2799-5875
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.
Quality Assessment of Seed Using Supervised Machine Learning Technique
J. Inst. Eng. India Ser. B
Kini M G, Ramanath (Autor:in) / Bhandarkar, Rekha (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 104 ; 901-909
01.08.2023
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Quality Assessment of Seed Using Supervised Machine Learning Technique
| Springer Verlag | 2023
Correction: Quality Assessment of Seed Using Supervised Machine Learning Technique
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Correction: Quality Assessment of Seed Using Supervised Machine Learning Technique
| Springer Verlag | 2024
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