Article

Applying Spectral Fractal Dimension to Predict the SPAD Value of Rice Leaves Under Disease Stress

2021-12-08

Abstract excerpt

<title>Abstract</title> <p>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 estimate the SPAD value by calculating the vegetation indices (VIs) through hy...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: eb0b2b8b-ffc4-560d-ae16-393766538cd2
DOI: 10.21203/rs.3.rs-1084650/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Applying Spectral Fractal Dimension to Predict the SPAD Value of Rice Leaves Under Disease StressDOI 10.21203/rs.3.rs-1084650/v1Read

Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stagesSemantic proximity Read

Review on phenotype traits analysis of Rice plants by using Machine Learning TechniquesSemantic proximity Read

Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurementsSemantic proximity Read

Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readingsSemantic proximity Read

Plant Leaf Disease Detection and Classification using Image Processing TechniquesSemantic proximity Read

Prediction of Diseases in Paddy Crop Using Machine Learning and Deep LearningSemantic proximity Read

Automated Phenotypic Trait Extraction for Rice Plant Using Terrestrial Laser Scanning Data.Semantic proximity Read

Optimized Multi-Class Rice Leaf Disease Classification Framework Using Rice Feature Selection (RiceFS) and Ensemble Machine Learning: Towards Sustainable AgricultureSemantic proximity Read

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insectsSemantic proximity Read

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics.Semantic proximity Read

Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning ModelsSemantic proximity Read

Aiming at decision making in plant disease protection and phenotyping by the use of optical sensorsSemantic proximity Read