Enabling hyperspectral imaging in diverse illumination conditions for indoor applications

Moghadam, Puria Azadi; Sharma, Neha; Hefeeda, Mohamed

doi:10.1145/3458305.3459594

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Various issues arise when capturing hyperspectral images in open fields owing to differences in environmental factors. These include changes in atmospheric conditions, uneven lighting sources [23,24], and noise caused by the heat of the sensor itself [25]. Therefore, accurate analysis of hyperspectral images obtained in open fields requires preprocessing, which involves setting and optimizing the hyperspectral sensor and imaging equipment according to the conditions.…”

Section: Hyperspectral Data Transformationsmentioning

confidence: 99%

Estimation of Apple Leaf Nitrogen Concentration Using Hyperspectral Imaging-Based Wavelength Selection and Machine Learning

Jang,

Han,

Cho

et al. 2023

Horticulturae

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In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors.

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Section: Hyperspectral Data Transformationsmentioning

confidence: 99%