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.
Highlights
IntroductionAgriculture management practices based on the concept of sustainable cropping ideas (such as reduced tillage intensity [10,11,12,13,14,15], fertilizer input [16], and organic farming [17,18]) combined with mixed cropping systems, legume-based, can effectively diminish greenhouse gas emissions by reducing the use of inorganic nitrogen fertilizers and replacing them with symbiotically fixed nitrogen [19], as well as carbon loss [5,20,21] and soil erosion [22] in cultivated soil
Our study highlights the capability of hyperspectral analysis for yield and biomass prediction in complex design fields through the use of two significant open-sourced software systems: the R language hyperspectral processing package and Python’s Auto-Sklearn machine learning technology
The performance evaluation with several types of hyperspectral vegetation indicators we employed to characterize crop production and straw mass was satisfactory. We suggest they can be further applied to other crop biophysical characteristics
Summary
Agriculture management practices based on the concept of sustainable cropping ideas (such as reduced tillage intensity [10,11,12,13,14,15], fertilizer input [16], and organic farming [17,18]) combined with mixed cropping systems, legume-based, can effectively diminish greenhouse gas emissions by reducing the use of inorganic nitrogen fertilizers and replacing them with symbiotically fixed nitrogen [19], as well as carbon loss [5,20,21] and soil erosion [22] in cultivated soil They can contribute to productivity and economic appeal to Northern European farmers, which is crucial for ensuring that these ecologically friendly systems can compete in terms of profitability with more traditional or artificially generated systems [23]. Owing to the variability in the structure, character, and husbandry of each experiment, investigations of VPT datasets can provide diverse outcomes [27]
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