Detection and Classification of Plant Diseases Using Soft Computing Techniques
- Resource Type
- Conference
- Authors
- Joshi, Praveen Kumar; Saha, Anindita
- Source
- 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2019 4th International Conference on. :1289-1293 May, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Diseases
Support vector machines
Agriculture
Feature extraction
Image segmentation
Training
Multiclass SVM
Soft computing
image processing
feature analysis
- Language
Accurate and fast detection of plant disease can be a great boon to crop yields. Curbing the complete cost to affordable amount is also a serious concern. The present manual technique for the detection of disease is a time consuming process and many times farmers with humble background can not afford it. Thus, an automation is needed to make this hectic process fast and well within budget of farmers with low budget. This paper discusses the monitoring of plant disease using image processing and soft computing techniques by taking samples of tomato leaves. In the initial phase, training dataset is created from the collected and enhanced images. Then, a test dataset is prepared arbitrarily and multiclass SVM is utilized for obtaining the classification results. This paper discusses the image segmentation method and feature analysis as well.