Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrim...
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MDPI AG
2025-07-01
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| author | Antonietta Eliana Barrasso Claudio Perone Roberto Romaniello |
| author_facet | Antonietta Eliana Barrasso Claudio Perone Roberto Romaniello |
| author_sort | Antonietta Eliana Barrasso |
| collection | DOAJ |
| description | The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. |
| format | Article |
| id | doaj-art-9f739014ec2a4d428a5490216c8c169f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-9f739014ec2a4d428a5490216c8c169f2025-08-20T03:36:31ZengMDPI AGApplied Sciences2076-34172025-07-011515853210.3390/app15158532Evaluation of Baby Leaf Products Using Hyperspectral Imaging TechniquesAntonietta Eliana Barrasso0Claudio Perone1Roberto Romaniello2Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, ItalyDepartment of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, ItalyDepartment of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, ItalyThe transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research.https://www.mdpi.com/2076-3417/15/15/8532hyperspectral imagingbaby leafprecision agriculturewater content |
| spellingShingle | Antonietta Eliana Barrasso Claudio Perone Roberto Romaniello Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques Applied Sciences hyperspectral imaging baby leaf precision agriculture water content |
| title | Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques |
| title_full | Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques |
| title_fullStr | Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques |
| title_full_unstemmed | Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques |
| title_short | Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques |
| title_sort | evaluation of baby leaf products using hyperspectral imaging techniques |
| topic | hyperspectral imaging baby leaf precision agriculture water content |
| url | https://www.mdpi.com/2076-3417/15/15/8532 |
| work_keys_str_mv | AT antoniettaelianabarrasso evaluationofbabyleafproductsusinghyperspectralimagingtechniques AT claudioperone evaluationofbabyleafproductsusinghyperspectralimagingtechniques AT robertoromaniello evaluationofbabyleafproductsusinghyperspectralimagingtechniques |