In Our project, a rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Septoria leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from Plan tVillage dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones.
In Existing, a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images.
This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method.
This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.
• The most of existing methods has ignored the poor quality images like images with noise or poor brightness.
• Less accuracy.
In Our proposed method, after preprocessing, Image is segmented using K-means clustering. Then GLCM(Gray Level Co-occurance Matrix) , Haralick and Gabor features are extracted and classified using SVM (Support Vector Machine) classifier.
• High accuracy is obtained and time consumption for detecting the shape.
• More datasets are included.
Processor Type : Pentium -IV
Speed : 2.4 GHZ
Ram : 128 MB RAM
Hard disk : 20 GB HD
Operating System : Windows 7
Software Programming Package : Matlab R2014a
‘Diagnosing plant problems: plant diseases and disorders’, available at https://firstdetector.org/static/pdf/NPDNDiagnosingPlantProblemsPlantDiseaseforreview2.pdf, accessed February 2017