Open AccessDiagnosis of Leaf Surface Disease Using Two Datasets of Tomato and Rice Obtained from Image Processing Techniques
Author(s) -
Seiyedeh Khadijeh Hosseiny,
Nasersadeghi Jola,
Seiyedeh Maryam Hosseiny
Publication year - 2021
Publication title -
review of computer engineering studies/review of computer engineer studies
Language(s) - English
Resource type - Journals
eISSN - 2369-0763
pISSN - 2369-0755
DOI - 10.18280/rces.080303
Subject(s) - preprocessor , feature selection , artificial intelligence , pattern recognition (psychology) , computer science , classifier (uml) , segmentation , image processing , image segmentation , data pre processing , data set , data mining , image (mathematics)
It is of a great importance in modern agriculture to study fast, automatic, inexpensive and accurate method of diagnosing plant diseasesTherefore, timely and accurately diagnosis of the disease in the fields is one of the most important factors in dealing with plant diseases. For this reason, in the present study, the image processing method study, has been examined for diagnosing the two important diseases of rice and tomato, brown spots and leaf blasts. In this study, firstly the data section is treated using improved k-means segmentation, after preprocessing. Secondly, comprehensive features are extracted and the disease areas are demarcated. An improved genetic algorithm is used in the feature selection step. Finally, images are categorized using the k-nearest neighbor’s algorithm (k-NN) classifier. The accuracy of the proposed method for the rice data set is 99.12 and for the tomato data set is 97.29, which shows a very good performance compared to other methods.