Automatic Detection of Tomato Leaf Deficiency and its Result of Disease Occurrence through Image Processing
S. Sivagami1, S. Mohanapriya2

1S. Sivagami, Assistant Professor, Department of Information Technology, Adhiyamaan College of Agriculture and Research, Hosur, Tamilnadu, India.
2Dr. S. Mohanapriya, Head of the Department, Department of Computer Science, KSR College of Arts and Science for Women, Tiruchengode, Tamilnadu, India.
Manuscript received on 22 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 4165-4172 | Volume-8 Issue-11, September 2019. | Retrieval Number: K15390981119/2019©BEIESP | DOI: 10.35940/ijitee.K1539.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

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.
Keywords: Deficiency detection, Segmentation, Features extraction, Classification and Disease prediction.
Scope of the Article: Image Processing and Pattern Recognition.