Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications
The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the cal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/1/202 |
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| author | Jaime Giménez-Gallego Juan D. González-Teruel Manuel Jiménez-Buendía Ana B. Toledo-Moreo Fulgencio Soto-Valles Roque Torres-Sánchez |
| author_facet | Jaime Giménez-Gallego Juan D. González-Teruel Manuel Jiménez-Buendía Ana B. Toledo-Moreo Fulgencio Soto-Valles Roque Torres-Sánchez |
| author_sort | Jaime Giménez-Gallego |
| collection | DOAJ |
| description | The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F<sub>1</sub> score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F<sub>1</sub> score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times. |
| first_indexed | 2024-12-10T19:11:16Z |
| format | Article |
| id | doaj.art-503a2d68c6d74cd7a1ab501160bdca46 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T19:11:16Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-503a2d68c6d74cd7a1ab501160bdca462022-12-22T01:36:44ZengMDPI AGApplied Sciences2076-34172019-12-0110120210.3390/app10010202app10010202Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture ApplicationsJaime Giménez-Gallego0Juan D. González-Teruel1Manuel Jiménez-Buendía2Ana B. Toledo-Moreo3Fulgencio Soto-Valles4Roque Torres-Sánchez5Group División de Sistemas e Ingeniería Electrónica (DSIE), Technical University of Cartagena, Campus Muralla del Mar s/n, E-30202 Cartagena, SpainGroup División de Sistemas e Ingeniería Electrónica (DSIE), Technical University of Cartagena, Campus Muralla del Mar s/n, E-30202 Cartagena, SpainGroup División de Sistemas e Ingeniería Electrónica (DSIE), Technical University of Cartagena, Campus Muralla del Mar s/n, E-30202 Cartagena, SpainGroup División de Sistemas e Ingeniería Electrónica (DSIE), Technical University of Cartagena, Campus Muralla del Mar s/n, E-30202 Cartagena, SpainGroup División de Sistemas e Ingeniería Electrónica (DSIE), Technical University of Cartagena, Campus Muralla del Mar s/n, E-30202 Cartagena, SpainGroup División de Sistemas e Ingeniería Electrónica (DSIE), Technical University of Cartagena, Campus Muralla del Mar s/n, E-30202 Cartagena, SpainThe crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F<sub>1</sub> score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F<sub>1</sub> score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.https://www.mdpi.com/2076-3417/10/1/202deficit irrigationcwsithermographyimage segmentationclusteringsvmdeep learningmodel training |
| spellingShingle | Jaime Giménez-Gallego Juan D. González-Teruel Manuel Jiménez-Buendía Ana B. Toledo-Moreo Fulgencio Soto-Valles Roque Torres-Sánchez Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications Applied Sciences deficit irrigation cwsi thermography image segmentation clustering svm deep learning model training |
| title | Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications |
| title_full | Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications |
| title_fullStr | Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications |
| title_full_unstemmed | Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications |
| title_short | Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications |
| title_sort | segmentation of multiple tree leaves pictures with natural backgrounds using deep learning for image based agriculture applications |
| topic | deficit irrigation cwsi thermography image segmentation clustering svm deep learning model training |
| url | https://www.mdpi.com/2076-3417/10/1/202 |
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