Search Results for author:
Found 11 papers, 1 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.
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.
SUTRA: A Novel Approach to Modelling Pandemics with Applications to COVID-19
The Covid-19 pandemic has two key properties: (i) asymptomatic cases (both detected and undetected) that can result in new infections, and (ii) time-varying characteristics due to new variants, Non-Pharmaceutical Interventions etc.
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.
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.
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.
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.
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks.
