ArticlePlant methods2022
Applying spectral fractal dimension index to predict the SPAD value of rice leaves under bacterial blight disease stress.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Article
- Detection technologies and sensing systems for crop pest identification and infestation severity prediction: a review.Frontiers in plant science · 2026Review
- Novel 1,3,4-Thiadiazole Derivatives: Synthesis, Antiviral Bioassay and Regulation the Photosynthetic Pathway of Tobacco against TMV Infection.International journal of molecular sciences · 2023Article
- Impact of the Alien Aquatic PlantPlants (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
backgroundThe chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil-plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress.
methodsIn this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods.
resultsThe results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R
conclusionsThis work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.
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