Application of support vector classifier for mango leaf disease classification

Abstract

In India, Mango is the fruit of high economic and ecological importance as it exports in large quantities. 1000 varieties of mangoes are cultivated and mostly supported commercially. Among all the Indian fruits, mangoes are highly demand. In majority of the Indian region, mango crops are suffering from several diseases that reduce both the production and the quality and parallel reduces its value on the international market. Mangoes are highly affected by number of diseases, which hamper its appearance, taste and has huge impact on the economy the Indian commercial growth rate has not raised. Manually identifying those disease is a complex task and time consuming, since lack of knowledge, poverty, infrastructure and the facilities the identification of the disease in earlier stages are not done by the farmers. In recent years, the plant pathologists apply different techniques to identify the diseases but then again these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. The disease has to diagnosed in order to provide solution to the farmers to increase the productivity with high quality. Currently, researchers have proposed several solutions to diagnosis of mango diseases automatically to gain high returns. The use of machine learning algorithms to identify diseases of plants from leaf photos is a very exciting field for advancement and research has carried in the proposed system using Support vector machine. Using non-linear SVC, achieved the accuracy of 88% for the dataset.