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.
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.
| Original language | English |
|---|---|
| Article number | 1114 |
| Number of pages | 26 |
| Journal | Remote Sensing |
| Volume | 14 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 24 Feb 2022 |
Bibliographical note
Funding Information:Acknowledgments: We give respect and gratitude to the scikit-learn framework’s developers and maintainers, as well as the Auto-sklearn interface developed at the University of Freiberg. This work was also supported by the Kuusiku Variety Testing Centre of Agricultural Research Centre in Estonia, and the Estonian IT Academy (English brand name StudyITin.ee) which has been financed by the European Social Fund.
Funding Information:
nia, and the Estonian IT Academy (English brand name StudyITin.ee) which has been financed by the European Social Fund. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest.
Funding Information:
This research was funded by the European Regional Development Fund within the Estonian National Programme for Addressing Socio-Economic Challenges through R&D (RITA): L180283PKKK, Estonian Research Council grant PUT PRG302, Estonian IT Academy financed by European Social Fund, and the Doctoral School of Earth Sciences and Ecology, financed by the European Union, European Regional Development Fund (Estonian University of Life Sciences ASTRA project ?Value-chain based bio-economy?).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
- hyperspectral
- automated machine learning
- vegetation index
- yield estimations
- biomass estimations
- precision agriculture
- narrowband
- spring wheat
- spring barley
- pea and oat