MAE-NIR: A masked autoencoder that enhances near-infrared spectral data to predict soil properties

Wan, Midi; Yan, Taiyu; Xu, Guoxia; Liu, Aibing; Zhou, Yangbin; Wang, Hao; Jin, Xiu

doi:10.1016/j.compag.2023.108427

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…The hyperspectral detection of soil nutrients has been given extensive research, and like detecting tree nutrients, it has also applied filtering, transformation [26,31], and sensitive band screening algorithms [27] to solve the problem of data redundancy. The impact of soil moisture has also been considered [33]. From the results, it can be found that it had high prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Wan et al proposed a spectral enhancement method based on a masked autoencoder to predict soil nutrients. This method can learn highly robust and generic spectral features from public NIR spectral datasets [33]. To overcome the influence of soil moisture, Lin et al proposed a method based on mixture-based weight learning to predict the soil total nitrogen [34].…”

Section: Rapid Detection Technology For Soil Nutrientsmentioning

confidence: 99%

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Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

Yuan,

Qi,

Huang

et al. 2024

Applied Sciences

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The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.

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