Detection of Corn Leaves Nutrient Deficiency Using Support Vector Machine (SVM)

Like other plants in general, corns are also requiring nutrients for their life. Nitrogen, phosphorus, and potassium are at least three main nutrients that all plants always need except corn. There are so many methods that can use to examine these three nutrients for corn through its leaves such as Leaf Color Chart (LCC), Chlorophyll Meters Soil Plant Analysis Development (SPAD), and Soil Test Kit. One method that is mostly used by farmers to examine nutrients content through corn leaves is used Leaf Color Chart (LCC) because it cost less than the other two. To overcome this problem, digital image processing could be a good solution that can be adopted by farmers to examine their plant's nutrients needs in an easier and cheaper way. In this study, the RGB extraction method of Hue, Saturation, Value (HSV) is proposed for a digital image processing system for corn leaves images. To classified its images result, Support Vector Machine (SVM) is used as a classification method for this study. By using this proposed method, an accuracy value of 80% is achieved to detect nutrients content in corn leaves.