外文摘要：Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand's unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 +/- 2 nm) and near-infrared (788 +/- 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 mu g center dot cm-2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.
外文关键词：Spectroscopy;nitrogen;precision;Smart farming;imagery
作者：Hussain, Tajamul;Pengphorm, Panuwat;Thongrom, Sukrit;Daengngam, Chalongrat;Duangpan, Saowapa;Boonrat, Pawita
作者单位：Oregon State Univ;Prince Songkla Univ;Publ Org
期刊名称：PLANTS-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：13（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台