Journals / Black Sea Journal of Agriculture / 2023 / Cilt: 6 Sayı: 5

Estimation of Soybean Seeds Weight Using Image Processing

Pages
511–515
Publication Date
2023-09-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However, as in some plants, this situation caused an increase in grain weight in soybeans.

📌 3 Key Takeaways

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Objective & Hypothesis:Today, image processing techniques are frequently used in irrigation, fertilization and spraying applications in order to increase agricultural input efficiency and product quality.
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Key Findings & Data:For this purpose, some image processing applications were carried out on the images of soybeans grown with the deficit irrigation (100%, 75, 50 and 25) method.
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Conclusion & Impact:However, as in some plants, this situation caused an increase in grain weight in soybeans.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Image processing · HSV color space · Soybean

Abstract

Today, image processing techniques are frequently used in irrigation, fertilization and spraying applications in order to increase agricultural input efficiency and product quality. In this study, the relationship between the image and weight of soybeans was investigated. For this purpose, some image processing applications were carried out on the images of soybeans grown with the deficit irrigation (100%, 75, 50 and 25) method. In the study, the relationship between the weight of soybeans and the number of pixels occupied on the images was 88.78%. The weights belonging to the displayed soybean grains decreased from 100% watered to 50% watered, in the 25% irrigated area, it increased again. The 25% irrigated case created significant stress for soybeans. However, as in some plants, this situation caused an increase in grain weight in soybeans.

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Cite This Article

KARADÖL, H., KUZU, H., KETEN, M. (2023). Estimation of Soybean Seeds Weight Using Image Processing. Black Sea Journal of Agriculture, Cilt: 6 Sayı: 5, 511–515. https://ajindex.com/articles/960221

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References (2)

  1. Acikgoz E, Sincik M, Wietgrefe G, Surmen M, Cecen S, Yavuz T, Erdurmus C, Goksoy AT. 2013. Dry matter accumulation and forage quality characteristics of different soybean genotypes. Turkish j Agri Forest, 37(1): 22-32.
  2. Agin O, Malasli MZ. 2016. The Place and importance of image processing techniques in sustainable agriculture. J Agri Machin Sci, 12(3): 199-206.