![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Leaf Disease Recognition Using Segmentation With Visual Feature Descriptor
Subscribe/Renew Journal
Agriculture has become the main sources of the income for many developed countries. The productivity in agriculture can be affected by various diseases present in the plant due to climatic conditions. The key step to improve the productivity of crops are to detect the disease at the preliminary stage. Automation becomes the best solution for this because it is more difficult to observe the disorders in plants parts. For that an image of affected plant leaf is acquired and segments the affected portion and to recognize the disease by using image processing and computer vision and machine learning techniques. The extracted features from the segmented portion are descripted using Global and Local Visual descriptors. Finally, we use the classifier to recognize the disease. Extracting a meaningful feature from an image is a central problem for a variety of computer vision problems like recognition, image retrieval, and classification. In this research, visual feature descriptor that best describe an image with respect to its visual property is explored. It is specifically focusing on recognizing tasks. The experimental results have proved that the combination of visual descriptors with various classifiers such as SVM and Ensemble Classifier produces high quality outcomes when compared to individual descriptors.
Keywords
Duck Search Optimization based Image Segmentation, Grey Level CoOccurrence Matrix, Scale-Invariant Feature Transform, Support Vector Machines, Ensemble Classifiers
Subscription
Login to verify subscription
User
Font Size
Information
- Adel Bakhshipour and Abdolabbas Jafari, “Evaluation of Support Vector Machine and Artificial Neural Networks in Weed Detection using Shape Features”, Computers and Electronics in Agriculture, Vol. 145, pp. 153-160, 2018.
- Anand H. Kulkarni and R.K. Ashwin Patil, “Applying Image Processing Technique to Detect Plant Diseases”,International Journal of Modern Engineering Research, Vol. 2, No. 5, pp. 3661-3664, 2012.
- A.K. Dey, M. Sharma and M.R. Meshram, “Image Processing based Leaf Rot Disease, Detection of Betel Vine (Piper BetleL.)”, Procedia Computer Science, Vol. 85, pp. 748-754, 2016.
- K.R. Gavhale and U. Gawande, “An Overview of the Research on Plant Leaves Disease Detection using Image Processing Techniques”, IOSR Journal of Computer Engineering, Vol. 16, No. 1, pp. 10-16, 2014.
- S. Gharge and P. Singh, “Image Processing for Soybean Disease Classification and Severity Estimation”, Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications, pp. 493-500, 2016.
- Harshal Waghmare; Radha Kokare and Yogesh Dandawate, “Detection and Classification of Diseases of Grape Plant using Opposite Colour Local Binary Pattern Feature and Machine Learning for Automated Decision Support System”, Proceedings of 3rd International Conference on Signal Processing and Integrated Networks, pp. 1-6, 2016.
- Y. Herdiyeni and A. Kusmana,“Fusion of Local Binary Patterns Features for Tropical Medicinal Plants Identification”, Proceedings of International Conference on Advanced Computer Science and Information Systems, pp. 352-357, 2013.
- C.S. Hlaing and S.M.M. Zaw, “Tomato Plant Diseases Classification using Statistical Texture Feature and Color Feature”, Proceedings of International Conference on Computer and Information Science, pp. 1-12, 2018.
- K. Jagan Mohan, M. Balasubramanian and S. Palanivel, “Detection and Recognition of Diseases from Paddy Plant Leaf Images”, International Journal of Computer Applications, Vol. 144, No. 12, pp. 1-13, 2016.
- S. Jana, S. Arijuvana Begum and S. Selvaganesan, “Design and Analysis of Pepper Leaf Disease Detection Using Deep Belief Network”, European Journal of Molecular and Clinical Medicine, Vol. 7, No. 9, pp. 1-12, 2020.
- A.A. Joshi and B. Jadhav, “Monitoring and Controlling Rice Diseases using Image Processing Techniques”, Proceedings of International Conference on Computing, Analytics and Security Trends, pp. 471-476, 2016 [12] Jun Liu, Fang Lv and Bobo Liu,“Identification Method of Sunflower Leaf Disease Based on SIFT Point”, Journal of Image and Graphics, Vol. 7, No. 2, pp. 1-13, 2019.
- B. Mishra, S. Nema and S. Nema, “Recent Technologies of Leaf Disease Detection using Image Processing ApproachA Review”, Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-5, 2017.
- M. Neumann, L. Hallau, B. Klatt, K. Kersting and C. Bauckhage, “Erosion Band Features for Cell Phone Image based Plant Disease Classification”, Proceedings of International Conference on Pattern Recognition, pp. 1-6, 2014.
- C.H. Ramesh Babu, Dammavalam Srinivasa Rao, V. Sravan Kiran and N. Rajasekhar, “Assessment of Plant Disease Identification using GLCM and KNN Algorithms”, International Journal of Recent Technology and Engineering, Vol. 8, No. 5, pp. 1-13, 2020.
- Sabah Bashir and Navdeep Sharma, “Remote Area Plant Disease Detection using Image Processing”, IOSR Journal on Electronics and Communication Engineering, Vol. 2, No. 6, pp. 31-34, 2012.
- Samiksha Devi and Bhanu Gupta, “GLCM-LBP Plant Leaf Disease Detection”, International Journal of Scientific Research and Engineering Development, Vol. 2, No. 3, pp. 1-13, 2019.
- Sandeep Kumar, “Plant Species Identification using SIFT and SURF Technique”, International Journal of Science and Research, Vol. 8, No. 3, pp. 1-14, 2018.
- T. Sathwik, R. Yasaswini, R. Venkatesh and A. Gopal, “Classification of Selected Medicinal Plant Leaves using Texture Analysis”, Proceedings of International Conference on Computing, Communications and Networking Technologies, pp. 1-6, 2013.
- N. Sengar, A. Srivastava and M.K. Dutta, “Machine Vision based Detection of Ageratum Enation Virus Infection using Light Microscopic Images of Poppy Plants Cells”, Proceedings of International Conference on Emerging Trends in Computing Communication Technologies, pp. 1-4, 2017.
- V. Singh and A.K. Misra, “Detection of Unhealthy Region of Plant Leaves using Image Processing and Genetic Algorithm”, Proceedings of International Conference on Advances in Computer Engineering and Applications, pp. 1028-1032, 2015.
- G. Tigistu and Y. Assabie, Y., “Automatic Identification of Flower Diseases using Artificial Neural Networks”, Proceedings of International Conference on Computer Engineering and Applications, pp. 1-5, 2015.
- L. Armi and S.F. Ershad, “Texture Image Analysis and Texture Classification Methods - A Review”, International Online Journal of Image Processing and Pattern Recognition, Vol. 2, No.1, pp. 1-29, 2019.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 237
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0