Area measurement of seed from distorted images for quality seed selection

Chaugule, Archana; Mali, Suresh N.

doi:10.1109/nuicone.2013.6780092

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The area calculation of seed from distorted images has been proposed in (A. Chaugule & Mali, 2013) for quality seed selection. The acquisition of the image, the addition of noise to the image, and the implementation of various image quality metrics are the major steps.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Seeds Categorization and Classification Based on Multiple Features-Set

Habib¹

2021

INDJCSE

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Seed classification contributes significantly to the final added value in crop production. Manual seed characteristics estimation is a difficult, time-consuming process that is prone to human error. Image processing is a good candidate for developing automated seed characteristics estimation systems. Many applications use image processing, such as inspecting pests in crop fields, grading fruits by size and color, estimating ripeness of fruits and vegetables before harvesting, and identifying crop diseases. For the classification of different seed varieties, old methods are still used. Manual seed size calculation and classification (small, medium, and large) with calipers, for example. There are no state-of-the-art methods for classifying seeds within species. This study will look at various image processing techniques for estimating various seed characteristics, improving classification, discovering new features, reducing complexity, and identifying flaws in existing techniques. According to the findings, there is a negative relationship between soya bean seed size and climatic factors. The decrease in osmotic ability affects larger seeds because they need more water for growth. As a result, seed selection is important prior to plantation based on environmental conditions.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Seeds Categorization and Classification Based on Multiple Features-Set

Habib¹

2021

INDJCSE

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“…The projected seed area is in most cases very good measurement parameter by itself, related to parameters such as weight or size [3]. The measurement of the area of irregularly shaped raw foods can be tedious [4].…”

Section: Introductionmentioning

confidence: 99%

Digital Separation of Occluded Seeds using Image Analysis

Chaugule¹,

Mali²

2015

IJCA

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The shape and size estimation done without separating the touching and overlapping seeds lead to inaccurate values of size and shape. The watershed segmentation and morphological processing suffers from problems like over segmentation and large processing times respectively. An algorithm based on concavities is developed and tested for segmentation of occluded paddy grains. The first step distinguished each seed in a binary image of a grain sample as either an isolated seed or a group of occluded seeds by using the shape properties. The next few steps separated individual seeds in binary images of occluded kernels. Split lines were drawn by the algorithm through the split points which were determined by evaluating the concavity of the corner points detected along the boundary, and selecting those points at which the concavity is highest. This approach is compared with morphological operations and watershed segmentation and the obtained results show that this method is effective in separating the touching seeds. And also after the separation the shape features extracted do not differ a lot from the actual shape features.

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