FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4214679098> ?p ?o ?g. }

• W4214679098 endingPage "1114" @default.
• W4214679098 startingPage "1114" @default.
• W4214679098 abstract "The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years." @default.
• W4214679098 created "2022-03-02" @default.
• W4214679098 creator A5001710847 @default.
• W4214679098 creator A5047338090 @default.
• W4214679098 creator A5047417992 @default.
• W4214679098 creator A5056720293 @default.
• W4214679098 creator A5067832699 @default.
• W4214679098 creator A5072901749 @default.
• W4214679098 creator A5077261922 @default.
• W4214679098 creator A5077334843 @default.
• W4214679098 creator A5091057651 @default.
• W4214679098 creator A5091391844 @default.
• W4214679098 date "2022-02-24" @default.
• W4214679098 modified "2023-10-18" @default.
• W4214679098 title "Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation" @default.
• W4214679098 cites W1567512734 @default.
• W4214679098 cites W1761723896 @default.
• W4214679098 cites W1964217023 @default.
• W4214679098 cites W1971495154 @default.
• W4214679098 cites W1973520843 @default.
• W4214679098 cites W1978223575 @default.
• W4214679098 cites W1984952346 @default.
• W4214679098 cites W1991230437 @default.
• W4214679098 cites W1991786119 @default.
• W4214679098 cites W1993022127 @default.
• W4214679098 cites W1994005439 @default.
• W4214679098 cites W2000102737 @default.
• W4214679098 cites W2011892677 @default.
• W4214679098 cites W2012686349 @default.
• W4214679098 cites W2016733723 @default.
• W4214679098 cites W2025967407 @default.
• W4214679098 cites W2039604550 @default.
• W4214679098 cites W2048303062 @default.
• W4214679098 cites W2053418288 @default.
• W4214679098 cites W2063623478 @default.
• W4214679098 cites W2070544336 @default.
• W4214679098 cites W2071383563 @default.
• W4214679098 cites W2091493105 @default.
• W4214679098 cites W2098247895 @default.
• W4214679098 cites W2101651903 @default.
• W4214679098 cites W2102539288 @default.
• W4214679098 cites W2109263120 @default.
• W4214679098 cites W2112442412 @default.
• W4214679098 cites W2125257725 @default.
• W4214679098 cites W2129483042 @default.
• W4214679098 cites W2130963558 @default.
• W4214679098 cites W2137608957 @default.
• W4214679098 cites W2139701068 @default.
• W4214679098 cites W2139925058 @default.
• W4214679098 cites W2142955757 @default.
• W4214679098 cites W2143401032 @default.
• W4214679098 cites W2148523105 @default.
• W4214679098 cites W2150566919 @default.
• W4214679098 cites W2159961845 @default.
• W4214679098 cites W2161527913 @default.
• W4214679098 cites W2161815745 @default.
• W4214679098 cites W2163410149 @default.
• W4214679098 cites W2168508773 @default.
• W4214679098 cites W2293936284 @default.
• W4214679098 cites W2309346848 @default.
• W4214679098 cites W2332981326 @default.
• W4214679098 cites W2594372785 @default.
• W4214679098 cites W2624443265 @default.
• W4214679098 cites W2648242067 @default.
• W4214679098 cites W2729039375 @default.
• W4214679098 cites W2736116482 @default.
• W4214679098 cites W2743931652 @default.
• W4214679098 cites W2768002338 @default.
• W4214679098 cites W2800185163 @default.
• W4214679098 cites W2805142011 @default.
• W4214679098 cites W2837916766 @default.
• W4214679098 cites W2897017401 @default.
• W4214679098 cites W2899720802 @default.
• W4214679098 cites W2904505555 @default.
• W4214679098 cites W2905239240 @default.
• W4214679098 cites W2919115771 @default.
• W4214679098 cites W2937040635 @default.
• W4214679098 cites W2949802254 @default.
• W4214679098 cites W2954212711 @default.
• W4214679098 cites W2963935416 @default.
• W4214679098 cites W2965743638 @default.
• W4214679098 cites W2966284335 @default.
• W4214679098 cites W2971287608 @default.
• W4214679098 cites W2983056308 @default.
• W4214679098 cites W2996024336 @default.
• W4214679098 cites W3003315949 @default.
• W4214679098 cites W3006820314 @default.
• W4214679098 cites W3007396174 @default.
• W4214679098 cites W3009017987 @default.
• W4214679098 cites W3012975023 @default.
• W4214679098 cites W3036446984 @default.
• W4214679098 cites W3044225531 @default.
• W4214679098 cites W3082085117 @default.
• W4214679098 cites W3092425241 @default.
• W4214679098 cites W3093009991 @default.
• W4214679098 cites W3099691904 @default.
• W4214679098 cites W3107647511 @default.
• W4214679098 cites W3115990005 @default.

• next