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Found 8 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 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.
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.
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.
