International Journal of Engineering Trends and Technology- Volume2Issue2- 2011 ISSN: 2231-5381 http://www.internationaljournalssrg.org Page 72 Color Feature Extraction of Tomato Leaf Diseases Jayamala K. Patil 1 Raj Kumar 2 1 Bharti Vidyapeeth C.O.E. Kolhapur, Ph.D. Scholar, Bhatati Vidyapeeth (Deemed Univ.) Pune 2 Defence Institute of Advanced Tech., Deemed University, Girinagar,Pune-25 ABSTRACT- In addition to environmental parameters like rain, temperature; diseases on crop is major factor which affects production quality & quantity of crop yield. Hence disease management is key issue in agriculture. For management of disease, it needs to be detected at early stage so as to treat it properly & control spread of the disease.. Because of advances in the technologies now a days it is possible to use the images of diseased leaf to detect the type of disease. This can be achieved by extracting features from the images which can be further used with classification algorithms or content based image retrieval systems. In this paper Color of the image is used to extract the features of images by calculating the 1 st ,2 nd & 3 rd moment of Color. Tomato leaves are used for experimentation. Keyword: Image Retrieval, Classification, Color I.INTRODUCTION India is an agricultural country wherein most of the population depends on agriculture. And agriculture is one of the major domain which decides economy of the nation. The quality & quantity of the agricultural production is affected by environmental parameters like rain, temperature & other weather parameters which are beyond control of human beings. Another major parameter which affects productivity of the crop is the disease where human beings can have control to improve the productivity for quality as well as for quantity. The diseases can be controlled by proper Disease management which is a challenging task. This challenge can be converted to easiest task by using image processing for detecting diseases of leaf, stem, root & fruit. With image processing it is possible to detect the affected area , type of disease & severity of the disease. Mostly diseases are seen on the leaves or stems of the plant. Because of the complexity of visual patterns of the diseases there has been increasing demand for development of more specific and sophisticated image pattern understanding algorithms which can be used for studies like classifying lesion, scoring quantitative traits, calculating area eaten by insects, etc. Now a days almost all of these tasks are processed manually or with distinct software packages. It is not only tremendous amount of work but also suffers from two major issues: i)excessive processing time and ii) subjectiveness rising from different individuals. Hence to conduct high throughput experiments, plant biologist need efficient computer software to automatically extract and analyze significant features . As far as the leaf of the plant is considered , the significant features can be obtained by ; 1. Color of the leaf 2. Texture of the leaf 3. Shape of the leaf 4. Color is one of the most widely used features[1]. Color features can be obtained by various methods like Color histogram[2,3], Color correlogram [4], Color R moment [1,5,6], Color structure descriptor[7]. The Color moment method has the lowest feature vector dimension and lower computational complexity. Hence it can be considered as suitable parameter to generate feature vectors which can be further used for classification purpose or for image retrieval. For the study proposed in this paper , Color of the diseased leaf of the tomato is used to generate the features. Tomatoes are the most popular & widely grown vegetable food crops in the World. Crops of tomatoes have socioeconomic importance to families, gardeners, farmers, laborers, marketers,