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.  

Hardy Kiwi, Actinidia arguta, Precision Agriculture

Tian, Z.; Zhang, Y.; Zhang, H.; Li, Z.; Li, M.; Wu, J.; Liu, K. Winter wheat and soil total nitrogen integrated monitoring based on canopy hyperspectral feature selection and fusion. Comput. Electron. Agric. 2022, 201, 107285.

Lin, P.; Song, C.; Yang, C.; Zhang, M.; Ma, S.; Wen, J.; Dong, D.; Han, Y. Rapid and accurate detection of total nitrogen in different types of soil using laser-induced breakdown spectroscopy combined with transfer learning. Comput. Electron. Agric. 2024, 226, 109396.

Yang, K.; Mo, J.; Luo, S.; Peng, Y.; Fang, S.; Wu, X.; Zhu, R.; Li, Y.; Yuan, N.; Zhou, C.; et al. Estimation of rice aboveground biomass by UAV imagery with photosynthetic accumulation models. Plant Phenomics 2023, 5, 0056.

Cheng, K.; Yan, J.; Li, G.; Ma, W.; Guo, Z.; Wang, W.; Li, H.; Da, Q.; Li, X.; Yao, Y. Remote sensing inversion of nitrogen content in silage maize plants based on feature selection. Front. Plant Sci. 2025, 16, 1554842.

Sun, H.; Feng, M.; Yang, W.; Bi, R.; Sun, J.; Zhao, C.; Xiao, L.; Wang, C.; Kubar, M.S. Monitoring leaf nitrogen accumulation with optimized spectral index in winter wheat under different irrigation regimes. Front. Plant Sci. 2022, 13, 913240.

Singh, R.; Kaur, S.; Joshi, D.R.; Iboyi, J.; Dar, E.; Sharma, L.K.; Singh, H. Estimating cotton biomass and nitrogen content using high-resolution satellite and UAV data fusion with machine learning. Smart Agric. Technol. 2025, 12, 101191.

Xi, R.; Gu, Y.; Zhang, X.; Ren, Z. Nitrogen monitoring and inversion algorithms of fruit trees based on spectral remote sensing: A deep review. Front. Plant Sci. 2024, 15, 1489151.

Yuanxiu, L.; Honglan, T.; Bing, Z.; Diya, L.; Xuan, Z.; Wantian, Y.; Jinming, F.; Yunting, Z.; Qing, C.; Yan, W.; et al. Comparative changes of health-promoting phytochemicals and sugar metabolism of two hardy kiwifruit (Actinidia arguta) cultivars during fruit development and maturity. Front. Plant Sci. 2022, 13, 1087452.

Morton, A.R. Kiwifruit (Actinidia spp.) Vine and Fruit Responses to Nitrogen Fertiliser Applied to the Soil or Leaves. 2013. Available online: https://mro.massey.ac.nz/bitstream/handle/10179/6856/02_whole.pdf (accessed on 20 July 2019).

Castilho, D.; Tedesco, D.; Hernandez, C.; Madari, B.E.; Ciampitti, I. A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species. Sci. Data 2024, 11, 585.

Swaminathan, V.; Thomasson, J.A.; Hardin, R.G.; Rajan, N.; Raman, R. Radiometric calibration of UAV multispectral images under changing illumination conditions with a downwelling light sensor. Plant Phenomics J. 2024, 7, 70005.

Nagpal, V.; Devare, M. Improving georeferencing accuracy in drone imagery: Combining drone camera angles with high and variable fields of view. QAJ 2025, 5, 228–246.

Stamford, J.D.; Vialet-Chabrand, S.; Cameron, I.; Lawson, T. Development of an accurate low cost NDVI imaging system for assessing plant health. Plant Methods 2023, 19, 9.

Kriegler, F.J. Preprocessing transformations and their effects on multispectral recognition. In Proceedings of the Sixth International Symposium on Remote Sensing of the Environment, Paphos, Cyprus, 26–29 March 2018; University of Michigan: Ann Arbor, MI, USA, 1969; pp. 97–131.

Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 351, 309. Available online: https://ntrs.nasa.gov/citations/19740022614 (accessed on 20 July 2019).

Park, Y.K.; Kim, S.H.; Kim, C.W.; Kim, J.H. Hardy Kiwifruit Cultivation Q&A; National Institute of Forest Science (NIOFS): Seoul, Republic of Korea, 2016.

Wang, S.; Guan, K.; Wang, Z.; Ainsworth, E.A.; Zheng, T.; Townsend, P.A.; Li, K.; Moller, C.; Wu, G.; Jiang, C. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. J. Exp. Bot. 2021, 72, 341–354.

Wang, X.; Shi, J. Leaf chlorophyll content is the crucial factor for the temporal and spatial variation of global plants leaf maximum carboxylation rate. Sci. Total Environ. 2024, 927, 172280.

Mitra, B.; Singha, P.; Roy Chowdhury, A.; Sinha, A.K.; Skalicky, M.; Brestic, M.; Alamri, S.; Hossain, A. Normalized difference vegetation index sensor-based nitrogen management in bread wheat (Triticum aestivum L.): Nutrient uptake, use efficiency, and partial nutrient balance. Front. Plant Sci. 2023, 14, 1153500.

Ali, A.M.; Ibrahim, S.M.; Salem, E.M.M.; El-Sadek, A.N.; Salem, H.M. In-season estimation of wheat response to nitrogen using normalized difference vegetation index. Int. J. Plant Prod. 2022, 16, 681–689.

Miller, J.O.; Mondal, P.; Sarupria, M. Sensor-based measurements of NDVI in small grain and corn fields by tractor, drone, and satellite platforms. Crop Environ. 2024, 3, 33–42.

Rodriguez, D.; Fitzgerald, G.J.; Belford, R.; Christensen, L.K. Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Aust. J. Agric. Res. 2006, 57, 781–789.

Fitzgerald, G.; Rodriguez, D.; O’Leary, G. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The Canopy Chlorophyll Content Index (CCCI). Field Crops Res. 2010, 116, 318–324.

Netto, A.T.; Campostrini, E.; Oliveira, J.G.; Bressan-Smith, R.E. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Sci. Hortic. 2005, 104, 199–209.

Gitelson, A.A.; Merzlyak, M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 1997, 18, 2691–2697.

Croft, H.; Chen, J.M.; Zhang, Y.; Simic, A.; Noland, T.L.; Nesbitt, N.; Arabian, J. Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework. ISPRS J. Photogramm. 2015, 102, 85–95.

Kong, W.; Huang, W.; Ma, L.; Li, C.; Tang, L.; Guo, J.; Zhou, X.; Casa, R. Biangular-Combined Vegetation Indices to Improve the Estimation of Canopy Chlorophyll Content in Wheat Using Multi-Angle Experimental and Simulated Spectral Data. Front. Plant Sci. 2022, 13, 866301.