Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes
Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in...
Ausführliche Beschreibung
Autor*in: |
Sharma, Ayush K. [verfasserIn] Sidhu, Simranpreet Kaur [verfasserIn] Singh, Aditya [verfasserIn] Zotarelli, Lincoln [verfasserIn] Sharma, Lakesh K. [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2024 |
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Anmerkung: |
© The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: American journal of potato research - Springer US, 1923, 101(2024), 5 vom: 10. Sept., Seite 394-413 |
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Übergeordnetes Werk: |
volume:101 ; year:2024 ; number:5 ; day:10 ; month:09 ; pages:394-413 |
Links: |
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DOI / URN: |
10.1007/s12230-024-09966-2 |
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Katalog-ID: |
SPR057763100 |
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520 | |a Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. | ||
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10.1007/s12230-024-09966-2 doi (DE-627)SPR057763100 (SPR)s12230-024-09966-2-e DE-627 ger DE-627 rakwb eng 630 640 VZ Sharma, Ayush K. verfasserin aut Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. Nitrogen (dpeaa)DE-He213 Phosphorous (dpeaa)DE-He213 Potassium (dpeaa)DE-He213 Spectroscopy (dpeaa)DE-He213 Sulfur (dpeaa)DE-He213 VNIR (dpeaa)DE-He213 Sidhu, Simranpreet Kaur verfasserin aut Singh, Aditya verfasserin aut Zotarelli, Lincoln verfasserin aut Sharma, Lakesh K. verfasserin (orcid)0000-0002-6220-5832 aut Enthalten in American journal of potato research Springer US, 1923 101(2024), 5 vom: 10. Sept., Seite 394-413 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:101 year:2024 number:5 day:10 month:09 pages:394-413 https://dx.doi.org/10.1007/s12230-024-09966-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 101 2024 5 10 09 394-413 |
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10.1007/s12230-024-09966-2 doi (DE-627)SPR057763100 (SPR)s12230-024-09966-2-e DE-627 ger DE-627 rakwb eng 630 640 VZ Sharma, Ayush K. verfasserin aut Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. Nitrogen (dpeaa)DE-He213 Phosphorous (dpeaa)DE-He213 Potassium (dpeaa)DE-He213 Spectroscopy (dpeaa)DE-He213 Sulfur (dpeaa)DE-He213 VNIR (dpeaa)DE-He213 Sidhu, Simranpreet Kaur verfasserin aut Singh, Aditya verfasserin aut Zotarelli, Lincoln verfasserin aut Sharma, Lakesh K. verfasserin (orcid)0000-0002-6220-5832 aut Enthalten in American journal of potato research Springer US, 1923 101(2024), 5 vom: 10. Sept., Seite 394-413 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:101 year:2024 number:5 day:10 month:09 pages:394-413 https://dx.doi.org/10.1007/s12230-024-09966-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 101 2024 5 10 09 394-413 |
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10.1007/s12230-024-09966-2 doi (DE-627)SPR057763100 (SPR)s12230-024-09966-2-e DE-627 ger DE-627 rakwb eng 630 640 VZ Sharma, Ayush K. verfasserin aut Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. Nitrogen (dpeaa)DE-He213 Phosphorous (dpeaa)DE-He213 Potassium (dpeaa)DE-He213 Spectroscopy (dpeaa)DE-He213 Sulfur (dpeaa)DE-He213 VNIR (dpeaa)DE-He213 Sidhu, Simranpreet Kaur verfasserin aut Singh, Aditya verfasserin aut Zotarelli, Lincoln verfasserin aut Sharma, Lakesh K. verfasserin (orcid)0000-0002-6220-5832 aut Enthalten in American journal of potato research Springer US, 1923 101(2024), 5 vom: 10. Sept., Seite 394-413 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:101 year:2024 number:5 day:10 month:09 pages:394-413 https://dx.doi.org/10.1007/s12230-024-09966-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 101 2024 5 10 09 394-413 |
allfieldsGer |
10.1007/s12230-024-09966-2 doi (DE-627)SPR057763100 (SPR)s12230-024-09966-2-e DE-627 ger DE-627 rakwb eng 630 640 VZ Sharma, Ayush K. verfasserin aut Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. Nitrogen (dpeaa)DE-He213 Phosphorous (dpeaa)DE-He213 Potassium (dpeaa)DE-He213 Spectroscopy (dpeaa)DE-He213 Sulfur (dpeaa)DE-He213 VNIR (dpeaa)DE-He213 Sidhu, Simranpreet Kaur verfasserin aut Singh, Aditya verfasserin aut Zotarelli, Lincoln verfasserin aut Sharma, Lakesh K. verfasserin (orcid)0000-0002-6220-5832 aut Enthalten in American journal of potato research Springer US, 1923 101(2024), 5 vom: 10. Sept., Seite 394-413 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:101 year:2024 number:5 day:10 month:09 pages:394-413 https://dx.doi.org/10.1007/s12230-024-09966-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 101 2024 5 10 09 394-413 |
allfieldsSound |
10.1007/s12230-024-09966-2 doi (DE-627)SPR057763100 (SPR)s12230-024-09966-2-e DE-627 ger DE-627 rakwb eng 630 640 VZ Sharma, Ayush K. verfasserin aut Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. Nitrogen (dpeaa)DE-He213 Phosphorous (dpeaa)DE-He213 Potassium (dpeaa)DE-He213 Spectroscopy (dpeaa)DE-He213 Sulfur (dpeaa)DE-He213 VNIR (dpeaa)DE-He213 Sidhu, Simranpreet Kaur verfasserin aut Singh, Aditya verfasserin aut Zotarelli, Lincoln verfasserin aut Sharma, Lakesh K. verfasserin (orcid)0000-0002-6220-5832 aut Enthalten in American journal of potato research Springer US, 1923 101(2024), 5 vom: 10. Sept., Seite 394-413 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:101 year:2024 number:5 day:10 month:09 pages:394-413 https://dx.doi.org/10.1007/s12230-024-09966-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 101 2024 5 10 09 394-413 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057763100</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241013064635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241013s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12230-024-09966-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057763100</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12230-024-09966-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="a">640</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sharma, Ayush K.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. 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author |
Sharma, Ayush K. |
spellingShingle |
Sharma, Ayush K. ddc 630 misc Nitrogen misc Phosphorous misc Potassium misc Spectroscopy misc Sulfur misc VNIR Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes |
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630 640 VZ Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes Nitrogen (dpeaa)DE-He213 Phosphorous (dpeaa)DE-He213 Potassium (dpeaa)DE-He213 Spectroscopy (dpeaa)DE-He213 Sulfur (dpeaa)DE-He213 VNIR (dpeaa)DE-He213 |
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ddc 630 misc Nitrogen misc Phosphorous misc Potassium misc Spectroscopy misc Sulfur misc VNIR |
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Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes |
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Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes |
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Sharma, Ayush K. |
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Sharma, Ayush K. Sidhu, Simranpreet Kaur Singh, Aditya Zotarelli, Lincoln Sharma, Lakesh K. |
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optimizing uav hyperspectral imaging for predictive analysis of nutrient concentrations, biomass growth, and yield prediction of potatoes |
title_auth |
Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes |
abstract |
Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Accurate real-time estimation of nutrient concentrations in potato (Solanum tuberosum L.) canopies is crucial for advanced decision support systems in site-specific nutrient management. This study investigated the effectiveness of unmanned aerial vehicle (UAV) based hyperspectral imaging in predicting nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) concentrations in potato plants comparing two sampling types such as petiole/leaves and above-ground biomass (AGB) sampling. Furthermore, this study also investigates the prediction of AGB, total, and marketable yield of two potato cultivars, 'Atlantic' (chipping) and 'Red La Soda' (tablestock). Four UAV flights over experimental sites were made, and hyperspectral imaging sensors (393–995 nm, 273 bands) were conducted, which coincided with the in-field sample collection as ground truth. Data were analyzed using the partial least square regression model after preprocessing and extracting spectra from images. The model showed high accuracy in estimating plant N concentration from petiole/leaf samples (external validation $ R^{2} $ = 0.58; [external validation RMSE = 0.31 × $ 10^{4} $ mg $ kg^{−1} $]), as well as for P (0.75 [0.05 × $ 10^{4} $ mg $ kg^{−1} $]) and S (0.58 [0.03 × $ 10^{4} $ mg $ kg^{−1} $]). Potassium estimation accuracy improved with biomass sampling (0.47 [1.19 × $ 10^{4} $ mg $ kg^{−1} $]). Above-ground biomass estimation had higher accuracy for 'Atlantic' (0.75 [1.29 Mg $ ha^{−1} $]) than for 'Red La Soda' (0.57 [1.38 Mg $ ha^{−1} $]). The model accurately estimated total and marketable tuber yields for both cultivars, with variations noted based on flight timing related to the crop stage. Cultivar ‘Red La Soda’ achieved the highest total yield accuracy on the first (0.76 [3.31 Mg $ ha^{−1} $]) and fourth flights (0.76 [3.31]), while the ‘Atlantic’ had the highest accuracy on the third flight (0.50 [4.11]). Model outputs, including standardized coefficients and variable importance in prediction, visualizing band contributions to measured parameter predictions are presented. This study concludes that hyperspectral imaging successfully estimates the potato nutrient concentration and predicts the in-season potato yield, which can contribute significantly to the potato management decision support system. However, it underscores the importance of multiyear high temporal data acquisition with variable potato varieties to establish a reliable AGB and yield estimation model to improve performance. © The Potato Association of America 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
collection_details |
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container_issue |
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title_short |
Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes |
url |
https://dx.doi.org/10.1007/s12230-024-09966-2 |
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|
score |
7.39985 |