First steps towards the spectral discrimination of diseases in barley (Hordeum vulgare L.)
One of the main causes of yield and quality loss in barley (Hordeum vulgare L.) crops are fungal diseases. Understanding the severity of these diseases is essential to achieve proper phytosanitary management. Monitoring is the practice used to assess the health status of crops. This is necessary for...
| Main Authors: | , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
| Published: |
Ediciones INTA
2024
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/20509 https://doi.org/10.58149/73at-xx42 |
| Summary: | One of the main causes of yield and quality loss in barley (Hordeum vulgare L.) crops are fungal diseases. Understanding the severity of these diseases is essential to achieve proper phytosanitary management. Monitoring is the practice used to assess the health status of crops. This is necessary for diagnosing and quantifying the level of attack, as well as for calculating the impact caused by diseases. This procedure is usually carried out through visual estimates and is therefore often subjective and imprecise. Remote sensing techniques emerge as a potentially useful alternative for detecting disease hotspots and differentiating areas with varying severity. The objectives were the following: to obtain the spectral signatures of healthy and diseased crops, to evaluate the feasibility of differentiating them using spectral indices, to identify the most sensitive bands for differentiation, and to compare the visual estimation of severity with that obtained through image classification. For this purpose, samples of healthy leaves and leaves showing symptoms of each of the foliar diseases caused by Drechslera teres, Rhynchosporium commune, and Bipolaris sorokiniana were used. Seventeen indices and the MDI were calculated from the spectral signatures obtained in the laboratory. The NDVI, CARI, NRI, OSAVI, RGR, and MDI indices showed significant differences between healthy and diseased leaves, but not between different diseases; only GNDVI was able to differentiate between them. The most sensitive bands identified by MDI were 440-490 nm and 645-680 nm. Regarding severity, the results showed a wide dispersion in visual estimates and the potential for using RGB image classification to quantify the intensity of disease symptoms. These results justify future field investigations to develop tools that contribute to improved phytosanitary management. |
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