Low-cost clamp for the measurement of vegetation spectral signatures

Acevedo-Correa, Camilo; Goez, Manuel; Torres-Madronero, Maria C.; Rondon, Tatiana

doi:10.1016/j.ohx.2024.e00557

HardwareX

2024

DOI: 10.1016/j.ohx.2024.e00557

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Low-cost clamp for the measurement of vegetation spectral signatures

Camilo Acevedo-Correa,

Manuel Goez,

Maria C. Torres-Madronero

et al.

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2024

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Characterization of Maize, Common Bean, and Avocado Crops under Abiotic Stress Factors Using Spectral Signatures on the Visible to Near-Infrared Spectrum

Goez,

Torres-Madronero,

Rondon

et al. 2024

Agronomy

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Abiotic stress factors can be detected using visible and near-infrared spectral signatures. Previous work demonstrated the potential of this technology in crop monitoring, although a large majority used vegetation indices, which did not consider the complete spectral information. This work explored the capabilities of spectral information for abiotic stress detection using supervised machine learning techniques such as support vector machine (SVM), random forest (RF), and neural network (NN). This study used avocados grown under various water treatments, maize submitted to nitrogen deficiency, and common beans under phosphorous restriction. The spectral characterization of the crops subjected to abiotic stress was studied on the visible to near-infrared (450 to 900 nm) spectrum, identifying discriminative bands and spectral ranges. Then, the advantages of using an integrated approach based on machine learning to detect abiotic stress in crops were demonstrated. Instead of relying on vegetation indices, the proposed approach used several spectral features obtained by analyzing the discriminative signature shape, applying a spectral subset band selection algorithm based on similarity, and using the minimum redundancy maximum relevance (MRMR), F-test and chi-square test ranks for feature selection. The results showed that supervised classifiers applied to the spectral features outperform the accuracies obtained from vegetation indices. The best common bean results were obtained using SVM with accuracies up to 91%; for maize and avocado, NN obtained 90% and 82%, respectively. It is noted that detection accuracy depends on various factors, such as crop type, genotype, and level of stress.

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Characterization of Maize, Common Bean, and Avocado Crops under Abiotic Stress Factors Using Spectral Signatures on the Visible to Near-Infrared Spectrum

Goez,

Torres-Madronero,

Rondon

et al. 2024

Agronomy

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show abstract

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Low-cost clamp for the measurement of vegetation spectral signatures

Cited by 1 publication

References 14 publications

Characterization of Maize, Common Bean, and Avocado Crops under Abiotic Stress Factors Using Spectral Signatures on the Visible to Near-Infrared Spectrum

Characterization of Maize, Common Bean, and Avocado Crops under Abiotic Stress Factors Using Spectral Signatures on the Visible to Near-Infrared Spectrum

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