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American Journal Of Agriculture And Horticulture Innovations

Peer Reviewed | Open Access | E-ISSN: 2771-2559
Published Article

Assessing Nitrogen Fertilizer Efficacy In Hardy Kiwi (Actinidia Arguta): A UAV-Multispectral Approach For Chlorophyll-Based Nutrient Monitoring

Assessing Nitrogen Fertilizer Efficacy In Hardy Kiwi (Actinidia Arguta): A UAV-Multispectral Approach For Chlorophyll-Based Nutrient Monitoring

Authors

  • Dr. Eleanor Vance
    Department of Remote Sensing and Geospatial Sciences, Wageningen University & Research, Wageningen, Netherlands
  • Prof. Ben Carter
    School of Agriculture and Food Sciences, The University of Queensland, Brisbane, Australia

Keywords:

Hardy Kiwi Actinidia arguta Precision Agriculture

Abstract

Background: Optimal nitrogen (N) management is crucial for the productivity and quality of hardy kiwi (Actinidia arguta), yet traditional methods for monitoring plant N status are often destructive and labor-intensive. Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer a non-destructive, high-throughput alternative for precision nutrient management. The strong correlation between leaf chlorophyll and nitrogen content provides a basis for spectrally estimating plant N status [17, 24]. This study aimed to develop and validate a UAV-based methodology to monitor fertilizer effects in hardy kiwi by estimating canopy chlorophyll content.

Methods: A field experiment was conducted in a hardy kiwi orchard with four distinct nitrogen fertilizer treatments. High-resolution multispectral imagery was acquired using a UAV platform at a key growth stage. Concurrently, ground-truth data, including leaf chlorophyll and nitrogen content, were collected from each experimental plot. A range of vegetation indices (VIs) derived from the multispectral data were calculated. Regression analysis was performed to build predictive models linking the VIs to the measured leaf chlorophyll content, and the models were validated using standard statistical metrics.

Results: The fertilizer treatments successfully established a significant gradient in leaf chlorophyll and nitrogen content. Strong correlations were observed between several VIs and the ground-truthed chlorophyll data. Red-edge based indices, such as the Canopy Chlorophyll Content Index (CCCI) [23], demonstrated the highest predictive power. The developed regression model accurately estimated leaf chlorophyll content with a high coefficient of determination (R2>0.80) and low Root Mean Square Error (RMSE). The resulting chlorophyll maps clearly visualized the spatial variability and differentiated the crop response across the N treatments.

Conclusion: UAV-based multispectral remote sensing is an effective and reliable tool for the non-destructive estimation of chlorophyll content in hardy kiwi canopies. This approach enables precise, in-season monitoring of plant nitrogen status, providing growers with actionable data for site-specific fertilizer management to enhance sustainability and productivity.  

 

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