Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging
The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (H...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/16/3317 |
| _version_ | 1797522149193285632 |
|---|---|
| author | Sourabhi Debnath Manoranjan Paul D. M. Motiur Rahaman Tanmoy Debnath Lihong Zheng Tintu Baby Leigh M. Schmidtke Suzy Y. Rogiers |
| author_facet | Sourabhi Debnath Manoranjan Paul D. M. Motiur Rahaman Tanmoy Debnath Lihong Zheng Tintu Baby Leigh M. Schmidtke Suzy Y. Rogiers |
| author_sort | Sourabhi Debnath |
| collection | DOAJ |
| description | The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves. |
| first_indexed | 2024-03-10T08:25:22Z |
| format | Article |
| id | doaj.art-611aae41158a40cbad85c79a392fc636 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:25:22Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-611aae41158a40cbad85c79a392fc6362023-11-22T09:35:48ZengMDPI AGRemote Sensing2072-42922021-08-011316331710.3390/rs13163317Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral ImagingSourabhi Debnath0Manoranjan Paul1D. M. Motiur Rahaman2Tanmoy Debnath3Lihong Zheng4Tintu Baby5Leigh M. Schmidtke6Suzy Y. Rogiers7Computer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, AustraliaComputer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, AustraliaNational Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, AustraliaComputer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, AustraliaComputer Vision Laboratory, School of Computing and Mathematics, Charles Sturt University, Bathurst 2795, AustraliaNational Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, AustraliaNational Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, AustraliaNational Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, AustraliaThe efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.https://www.mdpi.com/2072-4292/13/16/3317<i>Vitis vinifera</i>hyperspectral imagingnutrient deficiencyvariation index |
| spellingShingle | Sourabhi Debnath Manoranjan Paul D. M. Motiur Rahaman Tanmoy Debnath Lihong Zheng Tintu Baby Leigh M. Schmidtke Suzy Y. Rogiers Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging Remote Sensing <i>Vitis vinifera</i> hyperspectral imaging nutrient deficiency variation index |
| title | Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging |
| title_full | Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging |
| title_fullStr | Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging |
| title_full_unstemmed | Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging |
| title_short | Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging |
| title_sort | identifying individual nutrient deficiencies of grapevine leaves using hyperspectral imaging |
| topic | <i>Vitis vinifera</i> hyperspectral imaging nutrient deficiency variation index |
| url | https://www.mdpi.com/2072-4292/13/16/3317 |
| work_keys_str_mv | AT sourabhidebnath identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT manoranjanpaul identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT dmmotiurrahaman identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT tanmoydebnath identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT lihongzheng identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT tintubaby identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT leighmschmidtke identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging AT suzyyrogiers identifyingindividualnutrientdeficienciesofgrapevineleavesusinghyperspectralimaging |