| Autor: |
D. K. Kirange, Haridas D. Gadade |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). |
| DOI: |
10.1109/worlds450073.2020.9210294 |
| Popis: |
Indian economy is mostly dependent on agriculture. One of the highly used food crops in India is Tomato. Hence detection and analysis of leaf disease on tomato plants so as to increase the yield is highly essential. It becomes very hard to manually detect and analyze the tomato leaf diseases. Hence, in this paper we have proposed a segmentation-based approach for automatic segmentation of infected regions. The segmented area is further analyzed for disease classification and severity measurement. Leaf disease detection technique proposed here involves various stages including preprocessing, segmentation, feature extraction, training and classification followed by the severity measurement from the disease segmented region. We have analyzed the performance of different features extraction techniques including color, texture and shape features along with various classification techniques. The performance of the proposed system really inspires the farmers to use the automated system for detection and severity measurement of tomato plant disease. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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