Identification and classification of fungal disease affected on agriculture/horticulture crops using image processing techniques

This paper presents a study on the image processing techniques used to identify and classify fungal disease symptoms affected on different agriculture/horticulture crops. Many diseases exhibit general symptoms that are be caused by different pathogens produced by leaves, roots etc. Images Often do not possess sufficient details to assist in diagnosis, resulting in waste of time, misshaping the diagnostician to arrive at incorrect diagnosis. Farmers experience great difficulties and also in changing from one disease control policy to another i.e. intensive use of pesticides. Farmers are also concerned about the huge costs involved in these activities and severe loss. The cost intensity, automatic correct identification and classification of diseases based on their particular symptoms is very useful to farmers and also agriculture scientists. Early detection of diseases is a major challenge in horticulture / agriculture science. Development of proper methodology, certainly of use in these areas. Plant diseases are caused by bacteria, fungi, virus, nematodes, etc., of which fungi is the main disease causing organism. The present study has been focused on early detection and classification of fungal disease and its related symptoms.