Biology subjects

DeSalvio, A. J.

Publications and source records attributed to DeSalvio, A. J..

3 recordsLinked to original sources

bioRxiv2024

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

Senescence is a highly ordered degenerative biological process that affects yield and quality in annuals and perennials. Images from 14 unoccupied aerial system (UAS, UAV, drone) flights captured the senescence window across two experiments while functional principal component analysis (FPCA) effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: RCC and TNDGR. Convolutional neural networks (CNNs) trained on temporally concatenated, or "sandwiched," UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis (SPA). The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1-M6). Model performance was strongest for FPC1 scores from VSR (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to- moderate for RCC (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA). This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.

plant biology↗

bioRxiv2024

DEEP LEARNING-BASED HIGH-THROUGHPUT PHENOTYPING OF MAIZE (Zea mays L.) TASSELING FROM UAS IMAGERY ACROSS ENVIRONMENTS

AO_SCPLOWBSTRACTC_SCPLOWFlowering time is a critical phenological trait in maize (Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi-subjective and labor-intensive manual observation, limiting the scale and efficiency of genetics and breeding improvement. Leveraging unoccupied aerial system (UAS, also known as UAVs or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high-throughput phenotyping of tasseling in maize. Most CNN image analysis is overly complicated for simple tasks relevant to plant scientists. Here a methodology for extracting tasseling from RGB imagery using a CNN-based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas, U.S.A.) then validated through an unrelated set of hybrids. Overall accuracies of .946, .911, .985, and .988 were obtained for classifying maize images with or without tassels from College Station, TX in 2020; College Station, TX in 2021; Arlington, WI in 2021; and Madison, WI in 2021 respectively. By employing deep learning techniques, larger volumes of phenotypic data can be processed enabling high-throughput phenotyping in breeding programs. Although large datasets are required to train CNN models, the proposed methodology prioritizes simplicity in computational architecture while maintaining effectiveness in identifying flowered maize across diverse genotypes and environments.

plant biology↗

bioRxiv2023

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across- and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low GxE. A genome-wide association study (GWAS) implicated known (i.e., MADS69, ZCN8, sh1, wx1, du1) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key messageGenomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.

plant biology↗