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Tomato Nutrient Deficiency Detection on The Basis of Visible Symptoms Using Digital Image Processing

Nutrient deficiency may cause degradation in productivity of crop, the commercial plants like tomato usually gets affected by Nutrient deficiency. There is requirement of device which will predict Nutrient deficiency on the basis of visual symptoms. We have analysed tomato leaf using parameters like Uniformness detection (Deviation matrix method and Histogram analysis method), Lightness in colour detection, Chlorosis and Necrosis detection and by using some structural parameters like Status of Major vein, Length to Width ratio etc. On the basis of above parameters and PH of soil, we can accurately predict the Nutrient deficiency through which plant is suffering from. It is more relevant and non-destructive method of Nutrient deficiency detection. This method can detect deficiency at any stage of growth. Also similar techniques can be used for Nutrient deficiency detection of other plants like pomegranate, chilly, grape etc. Keywords—Nutrient deficiency, Tomato leaf processing, Image processing in Agriculture, Machine Vision in Agriculture, Deficiency Symptoms.