| Téléchargement | - Voir le manuscrit accepté : Regression based algorithms for predicting age of an Arabidopsis plant (PDF, 908 Kio)
|
|---|
| DOI | Trouver le DOI : https://doi.org/10.1109/CCECE.2017.7946620 |
|---|
| Auteur | Rechercher : Panjvani, Karim; Rechercher : Dinh, Anh; Rechercher : Wahid, Khan; Rechercher : Bhowmik, Pankaj1 |
|---|
| Affiliation du nom | - Conseil national de recherches du Canada. Développement des cultures et des ressources aquatiques
|
|---|
| Format | Texte, Article |
|---|
| Conférence | IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 30 April-3 May 2017, Windsor, ON, Canada |
|---|
| Sujet | age prediction; plant phenotyping; machine learning; pattern recognition in plant imaging |
|---|
| Résumé | This paper presents the analysis of various regression based machine learning algorithms for image-based plant phenotyping application and proposes a technique for plant phenotyping. Capability to predict age/development stage of a plant is one of the important factors for plant phenotyping and for analysis of in-situ crops. With the developed technique, these algorithms can predict age of an Arabidopsis plant based on the given images and mutant types. Publicly available dataset containing 165 images at different development stages and with various mutant types was used for this experiment. Results show that with this technique and different regression algorithms, it can achieve 92% prediction accuracy. Comparatively, linear regression algorithms show greater prediction accuracy than nonlinear algorithms. This method of age prediction can help plant scientists and breeders for better analysis of crops. |
|---|
| Date de publication | 2017-06-15 |
|---|
| Maison d’édition | IEEE |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Numéro du CNRC | NRC-ACRD-56315 |
|---|
| Numéro NPARC | 23002102 |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | d44b69a3-cfe5-4f67-8fe2-abedd9181ff5 |
|---|
| Enregistrement créé | 2017-08-18 |
|---|
| Enregistrement modifié | 2020-06-04 |
|---|
