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To setup a quick, error free and feasible method for diagnosing rice crop nutrition is very necessary for nutritional management. In this paper the support vector machine in approach of K-Means and Fuzzy C-Means clustering is used to identify mineral deficient leaf of Rice Crop. The images are collected from the database of International Rice Research Institute (IRRI). From these images 13 features were extracted using RGB mean value function and Regionprops. These function are used to identify different mineral deficient samples such as Nitrogen, Potassium, Phosphorus, Magnesium and Zinc. The combination of SVM classifier with K-Means and Fuzzy C-Means clustering, the mineral deficient leaf image of rice crop was identified with accuracy of 85% and 92% respectively. In context of computational time the Fuzzy C-Means Clustering with SVM achieve better performance than the K-Means clustering with SVM. This proposed technique plays a significant role to identify mineral deficiency in Rice Field by implementing in electronic gadgets such as Smartphone, Tablet etc.
Mineral deficiency of Rice crop;SVM;K-Means Clustering;Fuzzy C-Means Clustering.
Rice shows obvious symptoms when suffering from Nitrogen (N), Potassium (K), Magnesium (Mg), Phosphorous (P) and Zinc (Zn) nutritional deficiencies, and these symptoms are the basis of rapid morphological diagnoses in the field. Morphological diagnoses require large amounts of experience. This method cannot be quantified and suffers from poor operability; farmers find it very difficult to expertly use this method. Diagnostic methods extract information from the nutritional stress rice effectively and significantly, which can be used to automatically identify the nutrition status of rice . Mineral deficiency mostly appears on the leaves of rice crop including off-colored leaves, abnormal shape of leaves and also breaks down of a certain part of leaves. Mineral deficiency generally appears in the older leaves when nitrogen, phosphorus, potassium and magnesium are limiting.
The input images are correctly segmented and its feature vector is correctly extracted. FuzzyC-Means clustering is more accurate &noise free technique. Fuzzy C-Means with SVM is used and the deficiencies are successfully identified giving more accuracy in comparison to K-means. Here, the average accuracy of the classifier by using K-means and Fuzzy C-Means is found out to be 85% and 92% respectively. Also, the computational time in case of SVM with Fuzzy C-Means is less compared to its counterpart i.e. K-Means clustering. For more extensive analysis a large data set is required for training and testing purposes. The further research may be carry to the toxicity and also determine the degree of severity of mineral deficiency and toxicity in Rice Crop.
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