Non-destructive Estimation of Spinach Leaf Area: Image Processing and Artificial Neural Network Based Approach
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Title |
Non-destructive Estimation of Spinach Leaf Area: Image Processing and Artificial Neural Network Based Approach
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Creator |
Naveen Kumar Mahanti, Upendar Konga, Subir Kumar Chakraborty, V Bhushana Babu
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Subject |
ANN
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Description |
Not Available
Leaf area (LA) measurement provides valuable key information in understanding the growth and physiology of a plant. Simple, accurate and non-destructive methods are inevitable for leaf area estimation. These methods are important for physiological and agronomic studies. However, the major limitations of existing leaf area measurement techniques are destructive in nature and time consuming. Therefore, the objective of the present work is to develop ANN and linear regression models along with image processing techniques to estimate spinach leaf area making use of leaf width (LW) and length (LL) and comparison of developed models performance based on the statistical parameters. The spinach leaves were grown under different nitrogen fertilizer doses Not Available |
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Date |
2021-09-03T09:58:02Z
2021-09-03T09:58:02Z 2020-06-27 |
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Type |
Research Paper
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Identifier |
Mahanti, N. K., Konga, U., Chakraborty, S. K., & Babu, V. B. Non-destructive Estimation of Spinach Leaf Area: Image Processing and Artificial Neural Network Based Approach.r
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/61273 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Not Available
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