In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.
@article{arxiv.2006.11391,
title = {Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey},
author = {Akshay L Chandra and Sai Vikas Desai and Wei Guo and Vineeth N Balasubramanian},
journal= {arXiv preprint arXiv:2006.11391},
year = {2020}
}
Comments
Featured as an article at Journal of Advanced Computing and Communications, April 2020. arXiv admin note: text overlap with arXiv:1805.00881 by other authors