Deep Transfer Learning For Plant Center Localization
- URL: http://arxiv.org/abs/2004.13973v1
- Date: Wed, 29 Apr 2020 06:29:49 GMT
- Title: Deep Transfer Learning For Plant Center Localization
- Authors: Enyu Cai, Sriram Baireddy, Changye Yang, Melba Crawford, Edward J.
Delp
- Abstract summary: This paper investigates methods that estimate plant locations for a field-based crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs)
Deep learning approaches provide promising capability for locating plants observed in RGB images, but they require large quantities of labeled data (ground truth) for training.
We propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data.
- Score: 19.322420819302263
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Plant phenotyping focuses on the measurement of plant characteristics
throughout the growing season, typically with the goal of evaluating genotypes
for plant breeding. Estimating plant location is important for identifying
genotypes which have low emergence, which is also related to the environment
and management practices such as fertilizer applications. The goal of this
paper is to investigate methods that estimate plant locations for a field-based
crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs).
Deep learning approaches provide promising capability for locating plants
observed in RGB images, but they require large quantities of labeled data
(ground truth) for training. Using a deep learning architecture fine-tuned on a
single field or a single type of crop on fields in other geographic areas or
with other crops may not have good results. The problem of generating ground
truth for each new field is labor-intensive and tedious. In this paper, we
propose a method for estimating plant centers by transferring an existing model
to a new scenario using limited ground truth data. We describe the use of
transfer learning using a model fine-tuned for a single field or a single type
of plant on a varied set of similar crops and fields. We show that transfer
learning provides promising results for detecting plant locations.
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