Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique

Asefa, Bezuayehu Gutema; Tsige, Fikadu; Mehdi, Mina; Kore, Tamirat; Lakew, Aschalew

doi:10.1016/j.atech.2022.100097

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…We used extreme gradient boosting discriminant analysis (XGBDA) to determine the importance of each optimal wavelength for classification. XGBDA calculates variable importance by summing the reduction in loss function ("gain") at nodes where that variable was used for splitting, across all models [42]. The variables are then ranked by cumulative gain to identify the most influential wavelengths.…”

Section: Classification Performance Using Svm Models Combined With Wa...mentioning

confidence: 99%

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

Sanaeifar,

Yang,

Min

et al. 2024

Remote Sensing

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Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380–1022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology’s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields.

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Section: Classification Performance Using Svm Models Combined With Wa...mentioning

confidence: 99%

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

Sanaeifar,

Yang,

Min

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique

Cited by 1 publication

References 19 publications

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

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