Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices
The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to suppor...
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MDPI AG
2025-01-01
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| author | Maurizio Morisio Emanuela Noris Chiara Pagliarani Stefano Pavone Amedeo Moine José Doumet Luca Ardito |
| author_facet | Maurizio Morisio Emanuela Noris Chiara Pagliarani Stefano Pavone Amedeo Moine José Doumet Luca Ardito |
| author_sort | Maurizio Morisio |
| collection | DOAJ |
| description | The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones. A dataset of 4112 images, each having 2Mpixel resolution per tree and covering RGB, Red Edge, and near-infrared frequencies, was obtained from 185 hazelnut trees located in two different orchards of the Piedmont region (northern Italy). To increase accuracy, and especially to reduce false negatives, the image of each tree was divided into nine quadrants. For each quadrant, nine different vegetation indices (VIs) were computed, and in parallel, each tree quadrant was tagged as “healthy/unhealthy” by visual inspection. Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. Out of the nine VIs considered, only five (GNDVI, GCI, NDREI, NRI, and GI) were good predictors, while NDVI SAVI, RECI, and TCARI were not. Using them, a model accuracy of about 65%, with 13% false negatives was reached in a way that was rather independent of the algorithms, demonstrating that some VIs allow inferring the physio-pathological condition of these trees. These achievements support the use of drone-captured images for performing a rapid, non-destructive physiological characterization of hazelnut trees. This approach offers a sustainable strategy for supporting farmers in their decision-making process during agricultural practices. |
| format | Article |
| id | doaj-art-56e2fdb635c34c3bad4b0dda000cfa0f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-56e2fdb635c34c3bad4b0dda000cfa0f2025-01-10T13:21:28ZengMDPI AGSensors1424-82202025-01-0125128810.3390/s25010288Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation IndicesMaurizio Morisio0Emanuela Noris1Chiara Pagliarani2Stefano Pavone3Amedeo Moine4José Doumet5Luca Ardito6Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyInstitute for Sustainable Plant Protection, National Research Council, (IPSP-CNR), Strada delle Cacce, 73, 10135 Torino, ItalyInstitute for Sustainable Plant Protection, National Research Council, (IPSP-CNR), Strada delle Cacce, 73, 10135 Torino, ItalyDepartment of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyInstitute for Sustainable Plant Protection, National Research Council, (IPSP-CNR), Strada delle Cacce, 73, 10135 Torino, ItalyDepartment of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyDepartment of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyThe increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones. A dataset of 4112 images, each having 2Mpixel resolution per tree and covering RGB, Red Edge, and near-infrared frequencies, was obtained from 185 hazelnut trees located in two different orchards of the Piedmont region (northern Italy). To increase accuracy, and especially to reduce false negatives, the image of each tree was divided into nine quadrants. For each quadrant, nine different vegetation indices (VIs) were computed, and in parallel, each tree quadrant was tagged as “healthy/unhealthy” by visual inspection. Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. Out of the nine VIs considered, only five (GNDVI, GCI, NDREI, NRI, and GI) were good predictors, while NDVI SAVI, RECI, and TCARI were not. Using them, a model accuracy of about 65%, with 13% false negatives was reached in a way that was rather independent of the algorithms, demonstrating that some VIs allow inferring the physio-pathological condition of these trees. These achievements support the use of drone-captured images for performing a rapid, non-destructive physiological characterization of hazelnut trees. This approach offers a sustainable strategy for supporting farmers in their decision-making process during agricultural practices.https://www.mdpi.com/1424-8220/25/1/288aerial photosUAV<i>Corylus avellana</i>vegetation indicesnon-destructive analysesphenotyping |
| spellingShingle | Maurizio Morisio Emanuela Noris Chiara Pagliarani Stefano Pavone Amedeo Moine José Doumet Luca Ardito Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices Sensors aerial photos UAV <i>Corylus avellana</i> vegetation indices non-destructive analyses phenotyping |
| title | Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices |
| title_full | Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices |
| title_fullStr | Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices |
| title_full_unstemmed | Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices |
| title_short | Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices |
| title_sort | characterization of hazelnut trees in open field through high resolution uav based imagery and vegetation indices |
| topic | aerial photos UAV <i>Corylus avellana</i> vegetation indices non-destructive analyses phenotyping |
| url | https://www.mdpi.com/1424-8220/25/1/288 |
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