Distinguishing between closely related species of Allium and of Brassicaceae by narrowband hyperspectral imagery

Kang, Ye-Seong; Ryu, Chung Ho; Jun, Sae Rom; Jang, Si Hyeong; Park, Jun W.; Song, Hye Young; Sarkar, Tapash Kumar; Kim, Seong H.; Lee, Won Suk

doi:10.1016/j.biosystemseng.2018.10.003

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Remote sensing (RS) technology has been combined with spectroscopy for nondestructive monitoring of the crop yield and quality under actual environmental conditions [4]. Spectral reflectance data containing visible, red-edge, and near-infrared (NIR) spectral bands have tried to managing agriculture over a wide area [5] by helping predict the growth status [6]. This technology is expected to play a key role in integrated crop management systems [7].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

et al. 2021

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Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

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Section: Introductionmentioning

confidence: 99%

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

et al. 2021

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Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature

Kang

Jang

Park

et al. 2020

Computers and Electronics in Agriculture

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Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field

Cattaneo

Stellari

2019

Agronomy

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The last 10 years of knowledge on near infrared (NIR) applications in the horticultural field are summarized. NIR spectroscopy is considered one of the most suitable technologies of investigation worldwide used as a nondestructive approach to monitoring raw materials and products in several fields. There are different types of approaches that can be employed for the study of key issues for horticultural products. In this paper, an update of the information collected from the main specific International Journals and Symposia was reported. Many papers showed the use of NIR spectroscopy in the horticultural field, and the literature data were grouped per year, per product, and per application, such as studies of direct (chemical composition) and indirect (physical and sensorial) properties (P), process control (PC), and authenticity and classification studies (AC). A mention was made of a recent innovative approach that considers the contribution of water absorption in the study of biological systems.

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Distinguishing between closely related species of Allium and of Brassicaceae by narrowband hyperspectral imagery

Cited by 3 publications

References 26 publications

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature

Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field

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