PREDICTION OF MASS PRODUCTION OF FABA BEAN CROP USING DIGITAL IMAGE ANALYSIS
Accurate estimation of crop biomass is essential for assessing crop growth, yield potential, and optimizing agricultural management practices. Digital image analysis has emerged as a promising tool for non-destructive and efficient biomass prediction in crop production. In this study examine the pre...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
University of Agricultural Sciences and Veterinary Medicine, Bucharest
2024-01-01
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| Series: | Scientific Papers Series : Management, Economic Engineering in Agriculture and Rural Development |
| Online Access: | https://managementjournal.usamv.ro/pdf/vol.24_3/Art36.pdf |
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| Summary: | Accurate estimation of crop biomass is essential for assessing crop growth, yield potential, and optimizing agricultural management practices. Digital image analysis has emerged as a promising tool for non-destructive and efficient biomass prediction in crop production. In this study examine the predictive capabilities of digital image analysis for faba bean biomass estimation. Utilizing RGB (Red, Green, Blue) and vegetation indices image analysis techniques, the digital images was analyses of faba bean plant in fields to extract relevant biomass characteristics and quantify biomass. Through computational modelling and simulation, it assess the accuracy and reliability of these models across 100 days of growth and environmental conditions. The test analysis were conducted in the laboratory of the Agricultural Engineering Department. The results showed varying with the green biomass with the color indicators used, through which the green mass can be predicted. A linear equation appears relationship between normalized difference index and mass production during days of faba bean growth it was y = 6.0166x + 215.85 with R2 = 0.9495 . |
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| ISSN: | 2284-7995 2285-3952 |