Visual Analysis of Plant Growth using Transfer Learning

Abstract

Attention for monitoring growth of plants has been escalating in recent years, due to concerns on climate change and its impacts on the ecosystem. Understanding the growth characteristics of plants is vital for management of resources and optimization of crop yield in agricultural sector as well. Each plant population exhibits varying seasonal growth and reproduction manner with respect to the local environmental parameters. Using computational power of advancing technology is becoming unavoidable for the optimal usage of resources and human labor. In this paper, growth stages of plants are analyzed using recently advancing technologies of transfer learning. Deep learning is utilized for visual analysis of a variety of plants at different growing stages. The performance of a particular deep learning architecture is investigated for phenology recognition of agricultural plants at roughly three phenological stages, namely early, middle and late phenological stages.