33,005 research outputs found
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
In this paper, we investigate estimating emergence and biomass traits from
color images and elevation maps of wheat field plots. We employ a
state-of-the-art deconvolutional network for segmentation and convolutional
architectures, with residual and Inception-like layers, to estimate traits via
high dimensional nonlinear regression. Evaluation was performed on two
different species of wheat, grown in field plots for an experimental plant
breeding study. Our framework achieves satisfactory performance with mean and
standard deviation of absolute difference of 1.05 and 1.40 counts for emergence
and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants
from field images are better than the accuracy reported for the similar, but
arguably less difficult, task of counting leaves from indoor images of rosette
plants. Our results for biomass estimation, even with a very small dataset,
improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository:
https://github.com/p2irc/deepwheat_WACV-2018
Crop Yield Prediction Using Deep Neural Networks
Crop yield is a highly complex trait determined by multiple factors such as
genotype, environment, and their interactions. Accurate yield prediction
requires fundamental understanding of the functional relationship between yield
and these interactive factors, and to reveal such relationship requires both
comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop
Challenge, Syngenta released several large datasets that recorded the genotype
and yield performances of 2,267 maize hybrids planted in 2,247 locations
between 2008 and 2016 and asked participants to predict the yield performance
in 2017. As one of the winning teams, we designed a deep neural network (DNN)
approach that took advantage of state-of-the-art modeling and solution
techniques. Our model was found to have a superior prediction accuracy, with a
root-mean-square-error (RMSE) being 12% of the average yield and 50% of the
standard deviation for the validation dataset using predicted weather data.
With perfect weather data, the RMSE would be reduced to 11% of the average
yield and 46% of the standard deviation. We also performed feature selection
based on the trained DNN model, which successfully decreased the dimension of
the input space without significant drop in the prediction accuracy. Our
computational results suggested that this model significantly outperformed
other popular methods such as Lasso, shallow neural networks (SNN), and
regression tree (RT). The results also revealed that environmental factors had
a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics
and Operations Research (Baltimore, MD, USA). One of the winning solutions to
the 2018 Syngenta Crop Challeng
Recommended from our members
Fast and deep deformation approximations
Character rigs are procedural systems that compute the shape of an animated character for a given pose. They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance. When comparing film-quality character rigs with those designed for real-time applications, there is typically a substantial and readily apparent difference in the quality of the mesh deformations. Real-time rigs are limited by a computational budget and often trade realism for performance. Rigs for film do not have this same limitation, and character riggers can make the rig as complicated as necessary to achieve realistic deformations. However, increasing the rig complexity slows rig evaluation, and the animators working with it can become less efficient and may experience frustration. In this paper, we present a method to reduce the time required to compute mesh deformations for film-quality rigs, allowing better interactivity during animation authoring and use in real-time games and applications. Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions. The linear deformations are computed directly from the transformations of the rig's underlying skeleton. We use deep learning methods to approximate the remaining nonlinear portion. In the examples we show from production rigs used to animate lead characters, our approach reduces the computational time spent on evaluating deformations by a factor of 5×-10×. This significant savings allows us to run the complex, film-quality rigs in real-time even when using a CPU-only implementation on a mobile device
Demystifying Deep Learning: A Geometric Approach to Iterative Projections
Parametric approaches to Learning, such as deep learning (DL), are highly
popular in nonlinear regression, in spite of their extremely difficult training
with their increasing complexity (e.g. number of layers in DL). In this paper,
we present an alternative semi-parametric framework which foregoes the
ordinarily required feedback, by introducing the novel idea of geometric
regularization. We show that certain deep learning techniques such as residual
network (ResNet) architecture are closely related to our approach. Hence, our
technique can be used to analyze these types of deep learning. Moreover, we
present preliminary results which confirm that our approach can be easily
trained to obtain complex structures.Comment: To be appeared in the ICASSP 2018 proceeding
- …