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Home / programming language / Online Java Projects / Survey on classification and detection of plant leaf disease in agriculture environment
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Survey on classification and detection of plant leaf disease in agriculture environment

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SKU: PROJ8176 Categories: 2018-2019 Projects, Datamining Projects, Online Java Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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Survey on classification and detection of plant leaf disease in agriculture environment

Abstract– Agriculture is the cultivation and breeding of animals, plants and fungi for food, fibre, biofuel, medicinal plants and other products used to sustain and enhance human life. Agriculture is the backbone of India. But here we analysing plant disease prediction and classification of data plays very important role. In reality for importance have been a great support for making decisions in various fields especially in agriculture. Here different set of attributes based data is classified through some machine learning algorithms based finding frequent item sets. Technological importance have been a great support for making decisions in various fields especially in agriculture. The development of agriculture has been on under development for the past few years due to lack of Agriculture knowledge and environmental changes. The agricultural products can be broadly grouped into foods, fibers, fuels, and raw materials. Specific foods include cereals (grains), vegetables, fruits, oils, meats and spices. Fibers include cotton, wool, hemp, silk and flax. Clustering is a data mining technique which includes the task of grouping a set of objects such that the objects in the same group (called cluster) are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.

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