Automatic detection of tomato leaf deficiency and its result of disease occurrence through image processing

Abstract

Economic strength of a country is highly depends on agriculture productivity. More number of researches has been done based on detecting disease of plant by processing its leaf. However detecting disease after occurrence of it and identifying solution and source of it presence is a delay and useless process. The occurrence of disease in plants is due to absence of nutrient content in it. The main objective of this research work is to effectively detect deficiency in tomato leaf in order to protect it from disease occurrence. In our work, identification of deficiency in tomato leaf has been implemented using image processing technique. A plant grows in healthy way when it has its basic nutrients such as Nitrogen, phosphorus, potassium etc. Analyzing and detecting tomato plant leaf deficiency will identify occurrence of disease later. To detect deficiency in accurate way expectation and maximization segmentation process is implemented and features of segmented images have been extracted. Based on this extracted features classification is implemented to identify whether it is normal leaf or defected leaf. After identifying result the disease occurrence due to nutrient deficiency is shown. Therefore based on leaf image processing disease is identified by analyzing its deficiency in efficient way. Hence our research work prevents loss in tomato production and increases its growth and sales.