Geostatistics and digital image analysis for optimizing rice production
Rice, as the staple food for a significant majority of the Indonesian population, plays a crucial role in food security and socio-economic stability. To address the strategic challenges associated with rice production, this study focuses on utilizing geostatistics and digital image analysis techniqu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Little Lion Scientific
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28179/2/26Vol101No14.pdf http://eprints.utem.edu.my/id/eprint/28179/ https://www.jatit.org/volumes/Vol101No14/26Vol101No14.pdf |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | Rice, as the staple food for a significant majority of the Indonesian population, plays a crucial role in food security and socio-economic stability. To address the strategic challenges associated with rice production, this study focuses on utilizing geostatistics and digital image analysis techniques to optimize rice production and enhance agricultural practices. The key factors influencing rice production, including land area, fertilization, seed varieties, human resources, and agricultural technology, are examined in relation to food security concerns. Fertilizers, high-yielding varieties, and water availability emerge as vital elements for increasing national rice production. However, the efficiency and effectiveness of fertilization practices are heavily influenced by localized conditions, and current approaches often lack rationality and balance. To achieve efficient production and rationalize fertilization practices, this research proposes the application of geostatistics and digital image analysis techniques. Geostatistical models, specifically the Kriging Method, are employed to predict the spatial distribution of key nutrients and fertilizers, such as Sodium, Phosphorus, and Potassium (NPK), required by rice plants in paddy fields. Additionally, digital image processing and computer vision technologies are utilized to automate the assessment of nutrient adequacy based on leaf color analysis. This advancement replaces the previous manual comparison method, providing a more accurate and efficient approach. The integration of geostatistics and digital image analysis offers a promising solution to optimize nutrient management, precision fertilization, and overall rice production. By harnessing advanced technologies and data-driven approaches, this study aims to contribute to the development of sustainable agricultural practices, ensuring improved food security and socio-economic well-being for the Indonesian population. |
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