Automatic Detection of Tomato Leaf Deficiency using Soft Computing Technique
S. Sivagami1, S. Mohanapriya2

1S. Sivagami, Assistant Professor, Department of Computer Science, Adhiyamaan College of Agriculture and Research, Hosur, (Tamil Nadu), India.
2Dr. S. Mohanapriya, Head of the Department, Department of Computer Science, KSR College of Arts and Science for Women, Tiruchengode, (Tamil Nadu), India.
Manuscript received on November 26, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5406-5410  | Volume-9 Issue-2, December, 2019. | Retrieval Number: A1045109119/2019©BEIESP | DOI: 10.35940/ijeat.A1045.129219
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Abstract: Indian Economy mostly depends on Agriculture. Agriculture and its value-added products will occupy considerable amount of gross domestic product (GDP) and provides employment to more than half of the country’s workforce. Among all the countries India is one of the world’s largest producer of agriculture and horticulture crops. Among all the vegetables Tomato is one of the most important vegetable used to consume all over the world. Disease easily affect the tomato plant due to insects and nutrient deficiency. To detect nutrient deficiency using image segmentation and classification is the main focus of this paper. If detect nutrient deficiency in early stage then he yields increased and the disease caused due to lack of nutrient deficiency also reduced. In this paper k-means and Expectation maximization segmentation algorithms are used for segmentation and SVM classifier used for classification. Based on the results Expectation Maximation provide better result than K-means segmentation.
Keywords: Deficiency detection, k-means, Expectation-Maximization, SVM.