Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment

Abstract

• Integral difference model best fits the spatiotemporal variation of FIPAR in canopy. • Photometric sensor could monitor the spatiotemporal variation of crop canopy and PAR. • Monitoring the spatiotemporal variability of PAR in canopy represents crop growth. • The spatiotemporal variation of FIPAR is positively correlated with growth traits. Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are often inefficient and provide limited, outdated information. The photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth. It was designed to capture spatial variations in FIPAR across the canopy profile throughout the entire crop growth season. Subsequently, spatiotemporal models were applied to simulate variations in FIPAR across the entire canopy throughout the crop's growth. Finally, leveraging these model simulations, spatiotemporal variations in specific FIPAR values were derived to effectively characterize and describe crop growth dynamics. The technology was proved in a two-year monoculture cotton experiment. Results demonstrated that the post-simulation R² values of the dynamic spatiotemporal model were 0.940 for 2020 and 0.749 for 2021. Common agronomic traits used to measure cotton growth, including plant height (PH), aboveground biomass (AGB), and leaf area index (LAI), showed the highest correlations with FIPAR at 0.2 and 0.3 for PH, 0.5 and 0.6 for AGB, and 0.4 and 0.5 for LAI, all exhibiting significant positive relationships. Spatial variations of these FIPAR values within the canopy structure exhibited a linear relationship with PH, AGB, and LAI. This study demonstrated the feasibility of using photometric sensors as a non-destructive technology for real-time crop growth monitoring. The technology was developed to provide reasonably accurate crop growth information while balancing cost requirements for applications in both scientific research and agricultural production, offering high potential for guiding smart crop management to enhance agricultural productivity.

Plant phenotyping relevance

光量子センサーでキャノピーFIPARを取得し、時空間モデルで作物成長形質を推定する測定技術の開発・検証が研究の中心である。

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