Rule based approach to determine nutrient deficiency in paddy leaf images
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Abstract
Rice is one of the most consumed grains among the human society especially in Asia, but is easily affected by the deficiency caused due to lack of nutrient elements. Identifying nutrient deficiencies of paddy crop is very essential in overcoming these and thereby enhancing yield. Color of paddy leaves plays important role in identifying major deficiencies such as NPK (Nitrogen, phosphorus and Potassium) when crop is in its middle of its growth. In order to do so a database of healthy, nitrogen defected, phosphorus defected and potassium defected leaves is created. Color features of both healthy and defected paddy leaves are extracted using HSV color model. Similarly color features of test image is extracted and compared against database properties. Comparison results are validated against the rules set to determine the specific deficiency. The rules are framed based on rigorous experiment. The efficiency can be further increased by including leaf pattern analysis.
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