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Found 29 papers, 2 papers with code
Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress.
Evaluating NeRFs for 3D Plant Geometry Reconstruction in Field Conditions
We evaluate different Neural Radiance Fields (NeRFs) techniques for reconstructing (3D) plants in varied environments, from indoor settings to outdoor fields.
Latent Diffusion Models for Structural Component Design
Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions.
Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension
3D printing or additive manufacturing is a revolutionary technology that enables the creation of physical objects from digital models.
Deep learning powered real-time identification of insects using citizen science data
InsectNet can guide citizen science data collection, especially for invasive species where early detection is crucial.
Out-of-distribution detection algorithms for robust insect classification
One of the primary emphasis of researchers is to implement identification and classification models in the real agriculture fields, which is challenging because input images that are wildly out of the distribution (e. g., images like vehicles, animals, humans, or a blurred image of an insect or insect class that is not yet trained on) can produce an incorrect insect classification.
Neural PDE Solvers for Irregular Domains
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention.
Stochastic Conservative Contextual Linear Bandits
In this paper, we formulate a conservative stochastic contextual bandit formulation for real-time decision making when an adversary chooses a distribution on the set of possible contexts and the learner is subject to certain safety/performance constraints.
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs).
Differentiable Spline Approximations
Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer" in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis.
Distributed Deep Learning for Persistent Monitoring of agricultural Fields
This continuous increase in the amount of data collected has created both the opportunity for, as well as the need to deploy distributed deep learning algorithms for a wide variety of decision support tasks in agriculture.
Distributed Multigrid Neural Solvers on Megavoxel Domains
We specifically consider neural solvers for the generalized 3D Poisson equation over megavoxel domains.
Differentiable Programming for Piecewise Polynomial Functions
The paradigm of differentiable programming has considerably enhanced the scope of machine learning via the judicious use of gradient-based optimization.
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
The objective of this study is to develop a machine learning (ML) approach adept at soybean [\textit{Glycine max} L.
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements.
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping
For some images, the output of the interpretability methods indicated that spurious feature correlations may have been used to correctly classify them.
Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning
Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production including erratic rainfall and temperature variations.
How useful is Active Learning for Image-based Plant Phenotyping?
To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance.
Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions.
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one.
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
The first model is a WGAN model that uses a finite number of training images to synthesize new microstructures that weakly satisfy the physical invariances respected by the original data.
Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to its active layer morphology.
PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization
Additionally, we show how the software design of the framework allows easy extension to response surface reconstruction (Kriging), providing a high performance software for autonomous exploration.
Predicting County Level Corn Yields Using Deep Long Short Term Memory Models
Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest.
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
We identify the most sensitive wavelength as 733 nm using the saliency map visualization.
Interpretable Deep Learning applied to Plant Stress Phenotyping
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce.
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean
The focus of this work is to determine the minimal number of most effective hyperspectral bands that can distinguish between healthy and diseased specimens early in the growing season.
A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
In this paper, we propose a data-driven approach that is "gray box" i. e. that seamlessly utilizes expert knowledge in constructing a statistical network model for corn yield forecasting.
Deep Action Sequence Learning for Causal Shape Transformation
Deep learning became the method of choice in recent year for solving a wide variety of predictive analytics tasks.
