首页|Investigators from University of California Davis Zero in on Machine Learning [Nitrogen Retrieval In Grapevine (Vitis Vinifera L.) Leaves By Hyperspectral Sensing]
Investigators from University of California Davis Zero in on Machine Learning [Nitrogen Retrieval In Grapevine (Vitis Vinifera L.) Leaves By Hyperspectral Sensing]
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news originating from Davis, California, by NewsRx correspondents, research stated, “Nitrogen (N) is a critical macronutrient that directly affects grapevine yield and quality but is also highly mobile in soil and can cause environmental contamination when over-applied. Monitoring leaf N content is useful for assessing vine N status and thereby improving N management plans, and the ability to assess N by remote sensing is desirable.” Financial supporters for this research include USDA-NIFA Specialty Crop Research Initiative, California Table Grape Commission. Our news journalists obtained a quote from the research from the University of California Davis, “Remote sensing technology has been utilized for retrieving N in agronomic crops since the 1990s. However, remote sensing has not been widely adopted for N management mainly due to variability in spectral patterns that emerge when training a model on one dataset and then applying it to a different dataset. Differences in environmental conditions, phenological stages, and plant varieties contribute to variability. This study aimed to understand the major factors limiting the generalizability of N-level retrieval algorithms in grapevine: the impact of leaf age and how calibrating nitrogen content, using either leaf dry mass (Nmass) or leaf area (Narea), influence the outcomes. This study also compares the performance among five major N retrieval approaches. We used spectral data from a hand-held hyperspectral sensor measuring wavelengths from 350 to 2500 nm from 664 individual leaf samples obtained from two grapevine varieties on three different sampling campaigns. Our findings indicate that while machine learning and chemometric approaches offer high accuracy levels (R2 = 0.78), the physical modeling approach utilizing the PROSPECT radiative transfer model (RTM) is capable of consistently retrieving N independent of specific dataset conditions and with an acceptable level of accuracy (R2 = 0.45) while using only 50% of the hyperspectral data. The estimation of Nmass using Proteinmass, which was calculated from Proteinarea retrieved by RTM and ground truth LMA, demonstrates a high potential for consistent retrieval of Total N (TN) in grapes. We also found that while calibrating a model by Narea can eliminate the negative impact of leaf age on prediction accuracy, Nmass still performed better in four out of five approaches tested.”
DavisCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningNitrogenRemote SensingUniversity of California Davis
NETL
NSTL
Elsevier
