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Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection by Butte, Sujata; Vakanski, Aleksandar; Duellman, Kasia; Wang, Haotian; Mirkouei, Amin is a scholarly article available to read on EtoBox.

What is Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection about?

Recent research on the application of remote sensing and deep learning-based analysis in precision agriculture demonstrated a potential for improved crop management and reduced environmental impacts of agricultural production. Despite the promising results, the practical relevance of these technologies for field deployment requires novel algorithms that are customized for analysis of agricultural images and robust to implementation on natural field imagery. The paper presents an approach for analyzing aerial images of a potato (Solanum tuberosum L.) crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of healthy vs. stressed crop at a plant level. Specifically, we examine premature plant senescence resulting in drought stress on Russet Burbank potato plants. We propose a novel deep learning (DL) model for detecting crop stress, named Retina-UNet-Ag. The proposed architecture is a variant of Retina-UNet and includes connections from low-level semantic representation maps to the feature pyramid network. The paper also introduces a dataset of aerial field images acquired with a Parrot Sequoia camera. The dataset includes manually annotate

Author

Butte, Sujata; Vakanski, Aleksandar; Duellman, Kasia; Wang, Haotian; Mirkouei, Amin

Published

2021

Language

EN