Sensing and classification of rice (Oryza sativa L.) drought stress levels based on chlorophyll fluorescence

Title:

Sensing and classification of rice (Oryza sativa L.) drought stress levels based on chlorophyll fluorescence

Creator:

Xia, Q., Fu, L. J., Tang, H., Song, L., Tan, J. L., and Guo, Y.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c5de3f98-d531-4d60-9ff4-e19c04a9ccf7
uuid:c5de3f98-d531-4d60-9ff4-e19c04a9ccf7
doi:10.32615/ps.2022.005

Subject:

chlorophyll a fluorescence, drought stress, Ensemble model, K-Nearest Neighbors, and Support vector machine

Type:

model:article and TEXT

Format:

počítač and online zdroj

Description:

Sensing and classification of drought stress levels are very important to agricultural production. In this work, rice drought stress levels were classified based on the commonly used chlorophyll a fluorescence (ChlF) parameter (Fv/Fm), feature data (induction features), and the whole OJIP induction (induction curve) by using a Support Vector Machine (SVM). The classification accuracies were compared with those obtained by the K-Nearest Neighbors (KNN) and the Ensemble model (Ensemble) correspondingly. The results show that the SVM can be used to classify drought stress levels of rice more accurately compared to the KNN and the Ensemble and the classification accuracy (86.7%) for the induction curve as input is higher than the accuracy (43.9%) with Fv/Fm as input and the accuracy (72.7%) with induction features as input. The results imply that the induction curve carries important information on plant physiology. This work provides a method of determining rice drought stress levels based on ChlF.

Language:

Multiple languages

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

102-109

Source:

Photosynthetica | 2022 Volume:60 | Number:Special issue

Harvested from:

CDK

Metadata only:

false