Abstract:
We propose a data-driven method using a convolutional neural network (CNN) to classify plant growth stages, using Sentinel-1 SAR backscattering values and computer-genera...Show MoreMetadata
Abstract:
We propose a data-driven method using a convolutional neural network (CNN) to classify plant growth stages, using Sentinel-1 SAR backscattering values and computer-generated schematics representing plant development. Opposed to the physics-based approaches of BS simulation such as integral equation modelling, this approach is data-driven and has the potential to be more robust. A total of five field measurement campaigns were run over the five months and we collected the soil roughness and wheat canopy parameters. Data was used to randomly generate the synthetic images which were further used to train and test a CNN. Computer-generated plant schematics are digital representations of the soil and plant layer which are the two main components that affect the BS coefficients. Results show that the model successfully classified five stages of the wheat canopy growth with the highest test accuracy of 96.4%.
Date of Conference: 07-12 July 2024
Date Added to IEEE Xplore: 05 September 2024
ISBN Information: