International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 861 Android Based Plant Disease Identification System Using Feature Extraction Technique Dixit Ekta Gajanan 1 , Gavit Gayatri Shankar 2 , Gode Vidya Keshav 3 1,2,3 Student, Dept. of Computer Engineering, Gokhale Education Society's R. H. Sapat College of Engineering Management Studies and Research,Nashik, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Although professional agriculture engineers are responsible for the recognition of plant diseases, intelligent systems can be used for their diagnosis in early stages. The expert systems that have been proposed in the literature for this purpose, are often based on facts described by the user or image processing of plant photos in visible, infrared, light etc. The recognition of a disease can often be based on symptoms like lesions or spots in various parts of a plant. The color, area and the number of these spots can determine to a great extent the disease that has mortified a plant. Higher cost molecular analyses and tests can follow if necessary. This application can easily be extended for different plant diseases and different smart phone platforms. Key Words: Image processing, Intelligent system, Molecular analyses, Plant diseases, Smart phone. 1. INTRODUCTION The studies of fruit or plant can be determined by observable patterns of specific plant and it is critical to monitor health and detect disease within a plant. Through proper management strategies such as pesticides, fungicides and chemical applications one can facilitates control of diseases which interns improve quality. There are various techniques available such as spectroscopic and imaging technology are applied to achieve superior plant disease control and management. With smart farming today’s farmer can use decision tools and automation techniques which seamlessly integrate product, knowledge and services for better productivity, grading and surplus yield. The purpose of this paper is to monitor diseases on fruits or plants or plants or plants and suggest better solution for healthy yield and productivity and for this SURF Pattern Matching concept is used. System uses two image databases, one for training of already stored infected area image and other for execution of query images. Three fruits or plants namely grapes, apple and pomegranate have been used for research in this paper. As there is enormous economical loss in export business due to degraded quality of fruit and it also has a harmful impact on human health. Detection of Fruit Disease using color, texture analysis, gives a great platform for implementing a smart farming. The model when designed and implemented can be considered for enriching India as a smart country. 2. LITERATURE SURVEY Image Processing for Smart Farming: Detection of Disease and Fruit Grading, Authors (Monica Jhuria, Ashwani Kumar, And Rushikesh Borse), 2013. As there is wide need for agricultural industries improved yield of fruit is important, there is need of automated technique which will find disease on fruits or plants or plants or plants. For this artificial neural network methodology is suggested which can be helpful to categories fruit infection. K-Means clustering is applied to find diseased area on the fruit but it has disadvantage of sizable estimation load. It will encourage agronomist to build better production and make correct time to time judgment. A Review of Image Processing For Pomegranate Disease Detection, Authors (Manisha A. Bhange, Prof. H. A. Hingoliwala), 2015. This process suggests a solution for the recognition of pomegranate fruit disease and for that disease after detection is proposed. In this process, web based technique applied to help non experts in identifying fruit diseases which is depends on the picture representing the symptoms of the fruit. Farmers can take image of fruit disease and upload it to the system. Then the farmer will see the fruit is affected by bacterial blight or not. A Cost Effective Tomato Maturity Grading System using Image Processing for Farmers, Authors (Sudhir Rao Rupangadi, Ranjani B.S., Prathik Nagaraj,Varsha G Bhat), 2014. In this system, it classifies ripeness of fruit based on its color or texture. It involves current techniques mainly manual inspection which leads to errorious classification, which results in economic losses due to inferior produce in the market chain. There are short comings that are several methodologies but they require highly expensive setups and complicated procedures, overall accuracy is achieved up to 98%. Adapted Approach for Fruit Disease Identification using Images, Authors (Shiv Ram Dubey, Anandsingh Jalal). An adaptive approach is experimentally validated. The approach consist of steps and that are stated as; first step is k-means clustering technique which is applied for defect segmentation and second step involves some state of art features that are extracted from segmented image and then segmented image are classified into one of classes with the help of multi-class support vector machine. It achieves precision up to 93%.