Developing models to detect maize diseases using spectral vegetation indices derived from spectral signatures

Maize, a vital global crop, faces numerous challenges, including outbreaks. This study explores the use of spectral vegetation indices for the early detection of maize diseases in individual leaves based on crop phenology at the vegetative, tasselling, and maturity stages. The research was conducted...

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Bibliographic Details
Published in:The Egyptian journal of remote sensing and space sciences 2024-09, Vol.27 (3), p.597-603
Main Authors: Lammy Nkuna, Basani, George Chirima, Johannes, Newete, Solomon W., Nyamugama, Adolph, van der Walt, Adriaan Johannes
Format: Article
Language:English
Subjects:
Disease detection
Hyperspectral remote sensing
Maize diseases
Spectral vegetation indices
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Summary:Maize, a vital global crop, faces numerous challenges, including outbreaks. This study explores the use of spectral vegetation indices for the early detection of maize diseases in individual leaves based on crop phenology at the vegetative, tasselling, and maturity stages. The research was conducted in rural areas of Giyani in the Limpopo province, South Africa, where smallholder farmers heavily rely on maize production for sustenance. Fungal and viral diseases pose significant threats to maize crops, necessitating precise and timely disease detection methods. Hyperspectral remote sensing, with its ability to capture detailed spectral information, offers a promising solution. The study analysed spectral reflectance data collected from healthy and diseased maize leaves. Various vegetation indices derived from spectral signatures, including the Normalized difference vegetation index (NDVI), Anthocyanin Reflectance Index (ARI), photochemical Reflectance Index (PRI), and Carotenoid Reflectance Index (CRI) were investigated for their ability to show disease-related spectral variations. The results indicated that during the tasselling stage, the spectral differences had minimum absorption in the blue region. However, a distinct shift in spectral reflectance was observed during the vegetative stage with 70 % increase in reflectance. First derivative reflectance analysis revealed peaks at approximately 715 nm and 722 nm, which were useful in the discrimination of the different growth stages. Generalized Linear Models (GLM) with binomial link functions and Akaike Information Criterion (AIC) showed that individual vegetation indices performed equally well. NDVI (P
ISSN:1110-9823
DOI:10.1016/j.ejrs.2024.07.005