Publications by authors named "Tim B Brown"

Article Synopsis
  • High-resolution genotype to phenotype studies in plants are using deep learning techniques, particularly CNNs and LSTMs, to enhance plant classification and understand dynamic behaviors essential for breeding climate-ready crops.
  • The proposed CNN-LSTM framework combines deep learning for automatic feature extraction with LSTMs to analyze time-series plant images, allowing for improved accuracy in distinguishing different plant genotypes.
  • Results indicate that this innovative approach surpasses traditional image analysis methods and highlights the importance of temporal data, suggesting its potential applications in various areas of plant classification efforts.
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Plant respiration can theoretically be fueled by and dependent upon an array of central metabolism components; however, which ones are responsible for the quantitative variation found in respiratory rates is unknown. Here, large-scale screens revealed 2-fold variation in nighttime leaf respiration rate (R) among mature leaves from an Arabidopsis () natural accession collection grown under common favorable conditions. R variation was mostly maintained in the absence of genetic variation, which emphasized the low heritability of R and its plasticity toward relatively small environmental differences within the sampling regime.

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Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience traits from large collections of germplasm if software can integrate among the technologies. Traits could be Captured throughout development and across environments from multi-dimensional phenotypes, by applying Genome Wide Association Studies (GWAS) to identify causal genes and background variation and functional structural plant models (FSPMs) to predict plant growth and reproduction in target environments.

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