Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publication Type : Conference Paper
Publisher : IEEE
Source : 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)
Url : https://ieeexplore.ieee.org/abstract/document/9441805
Campus : Coimbatore
School : School of Engineering
Department : Electrical and Electronics
Year : 2021
Abstract : The agricultural domain in past few decades has seen a decrease in its productivity. The main cause for this was found to be an increase in plant diseases. Having diseases in plants is quite common, but due to improper care there have been serious effects on plants. But we cannot keep inspecting each and every plant present in thousands. Hence, in this work an approach is developed which provides faster and more accurate results of the detected plant leaves and its corresponding diseases. The proposed work approach uses various image processing techniques for recognising the plant leaf type and detecting disease. The system uses two different classification methods namely, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) and their performances are compared.
Cite this Research Publication : Veni, S., Anand, R., Mohan, D., & Sreevidya, P. (2021, March). Leaf recognition and disease detection using content based image retrieval. In 2021 7th international conference on advanced computing and communication systems (ICACCS) (Vol. 1, pp. 243-247). IEEE.