A Review on Machine Learning Classification Techniques for Plant Disease Detection
| Autor: | Shruthi U, V Nagaveni, B K Raghavendra |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Population Feature extraction Disease Fungus Machine learning computer.software_genre Convolutional neural network Crop education education.field_of_study biology business.industry Crop yield fungi food and beverages biology.organism_classification Plant disease Support vector machine Statistical classification Identification (information) Agriculture Artificial intelligence business computer Bacteria |
| Zdroj: | 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). |
| Popis: | In India, Agriculture plays an essential role because of the rapid growth of population and increased in demand for food. Therefore, it needs to increase in crop yield. One major effect on low crop yield is disease caused by bacteria, virus and fungus. It can be prevented by using plant diseases detection techniques. Machine learning methods can be used for diseases identification because it mainly apply on data themselves and gives priority to outcomes of certain task. This paper presents the stages of general plant diseases detection system and comparative study on machine learning classification techniques for plant disease detection. In this survey it observed that Convolutional Neural Network gives high accuracy and detects more number of diseases of multiple crops. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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