Measuring Apple Water Stress Using Temperature-Mapped 3D Imaging

Jenn Hoskins
21st September, 2024

Measuring Apple Water Stress Using Temperature-Mapped 3D Imaging

Image Source: Natural Science News, 2024

Key Findings

  • The study by the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) introduces a new fruit water stress index (FWSIEst) to better understand fruit water stress
  • Using advanced sensors, the study created 3D thermal images of apple canopies, accurately measuring fruit surface temperatures
  • The FWSIEst showed reliable results with low error margins and reflected increasing water stress in apples throughout the season
In the context of global warming and water scarcity, understanding the water status of fruit is increasingly crucial for fresh food production. A recent study conducted by the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) introduces a novel fruit water stress index (FWSI) to closely analyze the relationship between fruit and air temperatures[1]. This study utilizes advanced sensor systems to provide a more nuanced understanding of fruit water stress, potentially revolutionizing crop management practices. The research employs a sensor system that combines light detection and ranging (LiDAR) technology with thermal imaging to remotely analyze apple trees (Malus x domestica Borkh. "Gala"). The system generates 3D point clouds of the canopy, which are then geometrically calibrated to assign temperature values to each point. This results in a thermal point cloud of the entire canopy, allowing for the segmentation and annotation of fruit-specific point clouds. The estimated 3D distribution of fruit surface temperature (TEst) was found to be highly correlated with manually recorded reference temperatures (r2 = 0.93). The introduction of the fruit water stress index (FWSIEst) based on TEst represents a significant methodological innovation. This index provides more detailed information on fruit-specific water stress compared to the traditional crop water stress index, which is derived from whole-canopy 2D thermal imaging. The FWSIEst showed low error margins when compared to manual reference data, making it a reliable tool for assessing fruit water status. The study conducted extensive measurements involving 302 apples throughout the season and additional diel measurements on 50 apples, each recorded six times per day, totaling 600 apples. The results indicated that FWSIEst increased during the season, reflecting the progressive water stress experienced by the fruit. Notably, the FWSIEst calculated with air temperature plus 5°C exhibited diel hysteresis, capturing diurnal changes in fruit water status. This innovative approach builds on previous research that emphasizes the importance of continuous monitoring for effective irrigation management. For instance, earlier studies have highlighted the need for genotype-specific water management protocols, as different cultivars respond uniquely to water stress[2][3]. The current study's use of 3D spatiotemporal analysis aligns with these findings by offering a more precise and comprehensive method for monitoring fruit water status. Moreover, the study's findings are consistent with previous research on the benefits of continuous plant-based sensing for irrigation management[4]. By focusing on fruit-specific water stress, the FWSIEst provides a more targeted approach, potentially leading to more efficient water use and better crop yields. In conclusion, the introduction of the fruit water stress index (FWSIEst) by the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) represents a significant advancement in the field of applied ecophysiology. By leveraging advanced sensor technologies and 3D spatiotemporal analysis, this study offers a more detailed and accurate method for assessing fruit water status, thereby enhancing crop management practices in the face of global warming and water scarcity.

FruitsAgriculturePlant Science

References

Main Study

1) Fruit Water Stress Index of Apple Measured by Means of Temperature-Annotated 3D Point Cloud.

Published 19th September, 2024

https://doi.org/10.34133/plantphenomics.0252

Related Studies

2) Detecting Mild Water Stress in Olive with Multiple Plant-Based Continuous Sensors.

https://doi.org/10.3390/plants10010131

3) A Cultivar-Sensitive Approach for the Continuous Monitoring of Olive (Olea europaea L.) Tree Water Status by Fruit and Leaf Sensing.

https://doi.org/10.3389/fpls.2020.00340

4) Fruit and Leaf Sensing for Continuous Detection of Nectarine Water Status.

https://doi.org/10.3389/fpls.2019.00805


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