2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 18 , Issue 1 , PP: 204-225, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammad Arif Ali Usmani 1 * , Ausaf Ahmad 2
Doi: https://doi.org/10.54216/FPA.180114
Excessive use of fertilizers harms the environment and disrupts plant habitats, while also raising costs for farmers. Proper timing and amounts of nutrients are crucial for plant health and environmental balance. The greenness of rice leaves indicates their chlorophyll and nutrient levels. Agronomy studies show rice plants need 10 nutrients, including primary ones like Nitrogen (N), Phosphorus (P), and Potassium (K), and secondary ones like Iron (Fe), Manganese (Mn), Copper (Cu), Zinc (Zn), Boron (B), Molybdenum (Mo), and Chlorine (Cl). Leaf nitrogen concentration (LNC) is highly correlated with chlorophyll content. There are several tools on LEAF+ to measure it, such as leaf color (LCC), SPAD, chlorophyll or nitrogen. Since these tools are cost-effective and not available to all farmers, LCC offers farmers the ability to estimate plant nitrogen needs in real-time for efficient fertilizer use and increased rice yield. Notable innovation in agriculture is the Leaf Color Chart (LCC), developed by Japanese experts. It measures chlorophyll levels in rice plants and aids in nitrogen management without harming the plant. Today, LCC is used globally to improve production efficiency and optimize nitrogen application rates. The remaining 2 major nutrients potassium and phosphorus can also be measured by experimentally expanding the available database of LCC, as has been done in the two models developed in this research paper.
Leaf color Chart (LCC) , Nutrients Management in crops , Precision Agriculture , Agriculture Farm Monitoring , Machine Learning for Crop Health , Agricultural Machine Learning Models , Soil Nutrient Monitoring
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