Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming
This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models f...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/6233 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850160861995335680 |
|---|---|
| author | Giuseppe Altieri Sabina Laveglia Mahdi Rashvand Francesco Genovese Attilio Matera Alba Nicoletta Mininni Maria Calabritto Giovanni Carlo Di Renzo |
| author_facet | Giuseppe Altieri Sabina Laveglia Mahdi Rashvand Francesco Genovese Attilio Matera Alba Nicoletta Mininni Maria Calabritto Giovanni Carlo Di Renzo |
| author_sort | Giuseppe Altieri |
| collection | DOAJ |
| description | This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R<sup>2</sup>P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R<sup>2</sup>P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R<sup>2</sup> = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling. |
| format | Article |
| id | doaj-art-0028ea3dd41449f39e24fdb6ab3cdce0 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0028ea3dd41449f39e24fdb6ab3cdce02025-08-20T02:23:01ZengMDPI AGApplied Sciences2076-34172025-06-011511623310.3390/app15116233Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision FarmingGiuseppe Altieri0Sabina Laveglia1Mahdi Rashvand2Francesco Genovese3Attilio Matera4Alba Nicoletta Mininni5Maria Calabritto6Giovanni Carlo Di Renzo7Department of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyDepartment of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyCentre for Business and Industry Transformation, Nottingham Trent University, Nottingham NG1 4FQ, UKDepartment of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyDepartment of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyDepartment of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyDepartment of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyDepartment of Agricultural, Forestry, Food and Environmental Sciences (DAFE), University of Basilicata, 85100 Potenza, ItalyThis study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R<sup>2</sup>P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R<sup>2</sup>P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R<sup>2</sup> = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling.https://www.mdpi.com/2076-3417/15/11/6233portable near-infrared spectroscopyharvest quality predictionfruit ripening classificationmachine learning model |
| spellingShingle | Giuseppe Altieri Sabina Laveglia Mahdi Rashvand Francesco Genovese Attilio Matera Alba Nicoletta Mininni Maria Calabritto Giovanni Carlo Di Renzo Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming Applied Sciences portable near-infrared spectroscopy harvest quality prediction fruit ripening classification machine learning model |
| title | Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming |
| title_full | Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming |
| title_fullStr | Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming |
| title_full_unstemmed | Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming |
| title_short | Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming |
| title_sort | portable nir spectroscopy combined with machine learning for kiwi ripeness classification an approach to precision farming |
| topic | portable near-infrared spectroscopy harvest quality prediction fruit ripening classification machine learning model |
| url | https://www.mdpi.com/2076-3417/15/11/6233 |
| work_keys_str_mv | AT giuseppealtieri portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT sabinalaveglia portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT mahdirashvand portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT francescogenovese portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT attiliomatera portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT albanicolettamininni portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT mariacalabritto portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming AT giovannicarlodirenzo portablenirspectroscopycombinedwithmachinelearningforkiwiripenessclassificationanapproachtoprecisionfarming |