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  4. Vol 7, Issue 5, 2023

Research Progress in Potato Bud Recognition

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DOI: 10.23977/acss.2023.070507 | Downloads: 18 | Views: 429

Author(s)

Haifeng Wu 1

Affiliation(s)

1 Heilongjiang Bayi Agricultural University, Daqing, 163319, China

Corresponding Author

Haifeng Wu

ABSTRACT

In recent years, with the continuous development of agricultural technology, potato bud recognition technology has attracted more and more attention. Potato bud recognition is the key to the automatic cutting of potato seed tubers, which has a significant impact on the quality and yield of potatoes. Therefore, bud recognition technology is of great significance for the management and decision-making of agricultural production. At present, research on potato bud recognition mainly focuses on morphological methods, computer vision, and deep learning. Among them, morphological methods are mainly based on the theory of mathematical morphology, through the extraction and processing of morphological features of the bud eye to achieve recognition; Computer vision methods mainly use techniques such as image processing and feature extraction to achieve eye bud recognition; The deep learning method is mainly based on neural network models and achieves automatic recognition of eye buds through training with a large amount of data. In recent years, with the development of deep learning technology, potato bud recognition methods based on convolutional neural networks have become a research hotspot. By constructing a convolutional neural network model and using data enhancement, transfer learning and other methods, researchers have achieved a relatively significant recognition effect, but for potato bud eyes with attachments or mechanical damage on the surface, the recognition effect is general. Therefore, this article aims to review the current research progress of potato bud recognition, and seek a more efficient, accurate, and robust bud recognition technology that can provide better decision support for agricultural production, thereby improving the yield and quality of potatoes, reducing manual operation time and cost, and improving production efficiency and economic benefits.

KEYWORDS

Potato Sprouts; Image Recognition; Computer Vision; Feature Extraction; Deep Learning

CITE THIS PAPER

Haifeng Wu. Research Progress in Potato Bud Recognition. Advances in Computer, Signals and Systems (2023) Vol. 7: 42-45. DOI: http://dx.doi.org/10.23977/acss.2023.070507.

REFERENCES

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