Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra
Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic po...
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MDPI AG
2025-04-01
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| author | Zhaolong Hou Yaxuan Wang Feng Tan Jiaxin Gao Feng Jiao Chunjie Su Xin Zheng |
| author_facet | Zhaolong Hou Yaxuan Wang Feng Tan Jiaxin Gao Feng Jiao Chunjie Su Xin Zheng |
| author_sort | Zhaolong Hou |
| collection | DOAJ |
| description | Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy. |
| format | Article |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-7154976289bf4f25adb1fa2b75dc172c2025-08-20T02:18:10ZengMDPI AGPlants2223-77472025-04-01148119910.3390/plants14081199Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman SpectraZhaolong Hou0Yaxuan Wang1Feng Tan2Jiaxin Gao3Feng Jiao4Chunjie Su5Xin Zheng6College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Civil Engineering and Water Conservancy, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Horticulture and Landscape Architecture, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Civil Engineering and Water Conservancy, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaAccurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy.https://www.mdpi.com/2223-7747/14/8/1199nutrient deficiencycucumbersRaman spectroscopyprecision agricultureleaf positionnitrogen |
| spellingShingle | Zhaolong Hou Yaxuan Wang Feng Tan Jiaxin Gao Feng Jiao Chunjie Su Xin Zheng Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra Plants nutrient deficiency cucumbers Raman spectroscopy precision agriculture leaf position nitrogen |
| title | Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra |
| title_full | Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra |
| title_fullStr | Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra |
| title_full_unstemmed | Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra |
| title_short | Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra |
| title_sort | selection of optimal diagnostic positions for early nutrient deficiency in cucumber leaves based on spatial distribution of raman spectra |
| topic | nutrient deficiency cucumbers Raman spectroscopy precision agriculture leaf position nitrogen |
| url | https://www.mdpi.com/2223-7747/14/8/1199 |
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