Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images

Abstract

This study explored the use of TensorFlow image recognition model to identify herbaceous mimosa (Mimosa strigillosa) from digital images. There is a demand for such technology toward digital mapping of the spatial distribution of these important perennial legumes in the context of pasture management and as well as management of reclamation ground cover landscapes. This study provided evidence of successful application of TensorFlow model for identification of herbaceous mimosa from digital images with final accuracy of 95 % or more. The complexity of ground images of multiple objects in this study is suspected to induce fluctuations in validation accuracy. Such fluctuation of the validation accuracy, however, was shown to decline over time as the accuracy increased with more processing epochs involved. Despite the downside of intensive data preparation and heavy computing resources, the approach tested in this study is promising toward the next step of the technology application for identification of herbaceous mimosa patches from images acquired using Unmanned Aerial Vehicle (UAV).

Keywords

• Herbaceous mimosa
• Sunshine mimosa
• Powderpuff
• TensorFlow
• Machine learning
• Deep learning