Paddy Leaves Nutrient Deficiency Detection using Image Processing Technique
Image processing widely used in agriculture sector for finding problems like disease identification, weed detection, fruit grading etc., In this paper, work is carried out based on automatic deficiency detection of paddy leaves. Research of automatic leaf deficiency detection is essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect symptoms of deficiency as soon as they appear on plant leaves. Among all the grainsrice is one of the most consumed grain in south India, but is easily affected by the nutrition deficiency. To increase the yield early identification of nutrient deficiencies of paddy crop is very essential. Paddy leaves color plays an important role in identifying micro deficiencies such as CSM (Calcium, Sulfur and Magnesium) during middle stage of its growth. Database of healthy, calcium defected, sulfur defected and magnesium defected leaves is created to identify deficient paddy leaves. HSV color model is used to extract Color features of both healthy and defected paddy leaves. Color features of test image is also extracted and compared against database properties. Comparison results are checkedwith the rules set to decide the specific deficiency. The rules are framed based on thorough experiment.
Keywords - HSV, Image Processing, Calcium, Sulfur and Magnesium Deficiency