Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

Wang, Yuan; Ma, Hongbing; Wang, Jingzhe; Liu, Li; Pietikäinen, Matti; Zhang, Zipeng; Chen, Xiangyue

doi:10.1016/j.saa.2021.119739

Cited by 23 publications

(4 citation statements)

References 35 publications

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“…This advancement allows decision-makers to utilize computational intelligence to enhance the classification, monitoring, and nutritional value of crops in fields, resulting in billions of dollars in annual economic benefits [ 13 ]. Consequently, artificial intelligence algorithms (AIAs) based on data mining (DM) [ 18 ], deep learning (DL) [ 7 , 19 ], and machine learning (ML) [ 20 , 21 , 22 ] present promising techniques for future non-invasive pigment analyses in crop sciences and remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Falcioni

Antunes

Demattê

et al. 2023

Plants

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Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet–visible (UV–VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

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Section: Introductionmentioning

confidence: 99%

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Falcioni

Antunes

Demattê

et al. 2023

Plants

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“…However, all the above studies established traditional machine learning models based on the spectral features of crops. To further explore the degree of heavy metal contamination in soil, Wang et al [ 61 ] proposed an algorithm combining data augmentation (DA) and a deep learning network to classify the degree of chromium contamination in soil. The accuracy of DA-DNN reached 96.25%, proving that HSI technology combined with deep learning can realize large-scale detection of soil heavy metal pollution.…”

Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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“…In lettuce plants, they can be used to analyse hyperspectral data or ATR-FTIR spectroscopy data to accurately classify lettuce varieties, and to predict crop yield or quality [ 15 , 23 ]. Deep learning is a specific type of machine learning that can be particularly useful for these tasks, as it uses artificial neural networks to process and analyse data, and can learn to recognize complex patterns in the environment [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

Falcioni

Gonçalves

Oliveira

et al. 2023

Plants

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In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400–700 nm, 700–1300 nm, and 1300–2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.

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Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

Cited by 23 publications

References 35 publications

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

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