Main Article Content



Plant pathology is the scientific study of plant diseases caused by pathogens and environmental conditions. It includes pathogen identification, diseases cycles, economic impact, management of plant diseases, etc. In existing, to detect the diseases they used the spectroscopic techniques. These techniques are very expensive and can only be utilized by trained persons only. To overcome these problem, the detection of diseases which are detected using ELM (Extreme Learning Machine) technique. First the sample leaf image is given as input. Then, color channels are separated from the leaf image from these the green pixels are masked from the original image. The masking is done to avoid the processing of the green area of the leaves, since, it is healthy. By removing the green area from the original area the remaining infected area is calculated. Then the features are extracted from the affected area. Finally these features are given to the ELM to classify the disease. After finding the disease the solution for that disease is send to the corresponding user mobile using GSM device.


Leaf disease, fuzzy c-means clustering, GLCM, extreme learning machine

Article Details

How to Cite
SINGARAVELAN, S., ARUN, R., & MURUGAN, D. (2021). DISEASES DETECTION AND CLASSIFICATION OF AFFECTED PART OF PLANT LEAVES. Journal of Global Agriculture and Ecology, 11(1), 35-40. Retrieved from https://www.ikprress.org/index.php/JOGAE/article/view/6491
Case Reports / Case Studies


Rothe PR, Kshirsagar RV. Cotton leaf disease identification using pattern recognition techniques, International Conference on Pervasive Computing (ICPC); 2015.

Aakanksha Rastogi, Ritika Arora and Shanu Sharma. Leaf disease detection and grading using computer vision technology &fuzzy logic, 2nd International Conference on Signal Processing and Integrated Networks (SPIN); 2015.

Ratih Kartika Dewi, R. V Hari Ginardi. Feature extraction for identification of sugarcane rust disease, international conference on information, Communication Technology and System; 2014.

Bernardes JG, Rogeri N, Marranghello A, Pereira S. Identification of foliar diseases in cotton crop, Topics in Medical Image Processing and Computational Vision. 2013;8:67–85.

Ronse, Set-theoretical algebraic approaches to connectivity in continuous or digital spaces, Journal of Mathematical Imaging and Vision; 1998.

Ma Z, Ata K. K-nearest-neighbours with a novel similarity measure for intrusion detection, UKCI’13. 2013;266–271.

Maimon O, Rokach L. Data mining and knowledge discovery handbook, Second Edition; 2010.

Ruey-Hsia L, Geneva BG. Instability of decision tree classification algorithms, Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2002;570–575.

Leo BS, Breiman L. Random forests, Machine Learning. 2001;5–32.

Smita Naikwadi, Niket Amoda. Advances in image processing for detection of plant diseases, International Journal of Application or Innovation in Engineering & Management (IJAIEM). 2013;2(11).

Sannakki SS, Rajpurohit VS. Classification of pomegranate diseases based on back propagation neural network, International Research Journal of Engineering and Technology (IRJET). 2015;2(02).

Thangaduraiand K, Padmavathi K. Computer visionimage enhancement for plant leaves disease detection, World Congresson Computing and Communication Technologies; 2014.

Yuan Tian, Chunjiang Zhao, Shenglian Lu, Xinyu Guo. SVM-based multiple classifier system for recognition of wheat leaf diseases, Proceedings of 2010 Conference on Dependable Computing (CDC’); 2010.

Neetu Chahal, Anuradha. A clustering adaptive neural network approach for leaf disease identification, International Journal of Computer Applications. 2015;120(11)0975 – 8887.

Godliver Owomugisha, John A Quinn, Ernest Mwebaze, James Lwasa. Automated vision-based diagnosis of banana bacterial wilt disease and Black Sigatoka Disease Proceding of the 1’st Interational Conference on the use of Mobile ICT in Africa; 2014.