The majority of domesticated plant species are herbaceous annuals and woody perennials, yet many herbaceous perennial species hold potential for future agricultural systems. In addition to multiyear harvests, herbaceous perennials provide many ecosystem services, including erosion control as a result of their large and persistent root systems. However, the multiyear lifespan of perennial species has been a barrier to rapid domestication as breeding cycles require phenotyping over multiple growing seasons. Using phenomic selection, high-dimensional secondary traits measured on seedlings could be used to develop relationship matrices among individuals which are then used to predict field traits. Additionally, these models can serve as the selection criteria to identify individuals to advance to the next (pre)breeding generation, thus shortening the breeding cycle. This project substitutes costly genomics data with high-dimensional phenomics data asking: Can elite individuals of perennial species be predicted by phenomic relatedness models based on high-dimensional traits recorded on seedlings? To date, we have imaged 2280 seedlings from each of the following three perennial crop candidate species: intermediate wheatgrass (Thinopyrum intermedium), sainfoin (Onobrychis viciifolia), and silphium (Silphium integrifolium) on the Bellwether Foundation Phenotyping Facility housed at the Danforth Center. The images were processed using PlantCV to generate high-dimensional color and near-infrared profiles for each plant on each image day. Additionally, profiles were generated with handheld spectrometers. This work re-imagines innovations in plant traits, kinship matrices, genomic selection, phenotyping centers, and ultimately domestication, in order to expedite the development of an emerging generation of climate resilient, ecologically sustainable crops.
Automated monitoring and evaluation systems for plant phenotyping are one of the keys to advance and strengthen crop breeding programs. In this study, the improvements of the camera-based sensor system and a weather station from a previous study-assembled mainly from Raspberry Pi products-board with dual cameras (RGB and NoIR) providing high spatial and temporal resolution data-is outlined. Hardware for the internet connection and the power supply system of the sensor were upgraded. Previously, the sensor could automatically capture plant images following user-defined time points; thus, an image processing algorithm (edge computing) was developed and installed to extract digital phenotypic traits from the images after capturing process. With the development, the new sensor system could be integrated with the internet, and a cloud server was configured to store data online (digital traits and raw images). A real-time monitoring system was created to visualize the time series data of a trait development and plant images throughout the season. With such a system, plant breeders will be able to monitor multiple trials for timely crop management and decision-making process, which is also resources efficiency.
This is an adjustment to try a revision. Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure. We are met on a great battle-field of that war. We have come to dedicate a portion of that field, as a final resting place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this.But, in a larger sense, we can not dedicate -- we can not consecrate -- we can not hallow -- this ground. The brave men, living and dead, who struggled here, have consecrated it, far above our poor power to add or detract. The world will little note, nor long remember what we say here, but it can never forget what they did here. It is for us the living, rather, to be dedicated here to the unfinished work which they who fought here have thus far so nobly advanced. It is rather for us to be here dedicated to the great task remaining before us -- that from these honored dead we take increased devotion to that cause for which they gave the last full measure of devotion -- that we here highly resolve that these dead shall not have died in vain -- that this nation, under God, shall have a new birth of freedom -- and that government of the people, by the people, for the people, shall not perish from the earth.
The application of herbicides in agriculture has significantly increased in recent decades. While many herbicides improve the efficiency and efficacy of weed control, their excessive use at the wrong growth stage can cause crop foliar damage, higher input cost and negative environmental footprints. There are limited techniques to accurately monitor herbicide effects. Visual ratings are highly subjective and require extensive training or experience. High-throughput digital imaging is a promising tool to measure plant herbicide interaction in field crops. In this study, proximal and aerial based advanced sensors have been utilized to evaluate different herbicide modes-of-action in two model species, tame oat [Avena sativa; model for wild oat (Avena fatua)] and oriental mustard [Brassica juncea; model for wild mustard (Sinapis arvensis)]. The experimental trials were performed at three agro-climatic locations in Canada (Lethbridge (AB), Saskatoon (SK), and Lacombe (AB)). The proximal and UAV multispectral imagery data were collected for baseline (before treatment) and 1, 3, 7, 10, 14 and 21 days after treatments (DAT), alongside visual ratings. The Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index, Chlorophyll Vegetation Index, and Optimized Soil Adjusted Vegetation Index were used to assess variation of different DAT pigment content (photosynthetic rate) and chlorosis (damage %) in plot vegetation. The variation in obtained temporal indices (NDVI) suggest that the developed technology has potential to replace visual ratings (R2 ≈0.65-0.94) and can be used as a rapid screening tool for herbicide activity. Therefore, remote sensing tools could improve the precision and consistency of future herbicide assessments.
The automatic, and accurate plant phenotyping plays important role to improve the crop yield through enabling efficient plant analysis and plant breeding studies. The 3d deep learning allows automatic segmentation of plant parts from point cloud data. However, the network architecture is designed manually and performance is limited to prior experience. The aim of this study is to search for optimal 3d deep networks to perform the plant part segmentation. We perform the 3d neural architecture search by training a super network composed of candidate networks. Using the trained super network, the evolutionary searching is used to search for top performing architecture. The results demonstrate the searched architecture outperforms manually designed architectures by attaining mean IoU and accuracy of more than 90% and 96%, respectively. The searched architecture achieves more than 83% class-wise IoU for all main stem, branches, and boll class. This plant part segmentation method shows promising results and holds potential to be utilized by plant breeders for enhancing the production quality.
Crop improvement over the last few decades, especially after the Green Revolution, is partially driven by the intensive application of less expensive inorganic nitrogen (N) fertilizer. However, the unsustainable use of inorganic N fertilizer in crop production decreases farming profitability and creates a series of ecological burdens. One of the long-standing goals of crop breeding is to increase crops' nitrogen use efficiency (NUE). Studies have shown a number of phenotypic variations of sorghums grown in different N conditions, including root architecture, leaf parameters, growth parameters, yield, and biochemistry traits. Additionally, previous studies showed that the demand for N varies during the sorghum developmental stages, indicating a dynamic genetic control. In our study, taking advantage of the CRISPR-based gene editing and UNL's automatic high throughput phenotyping platform, we generated five edited sorghum lines under TX430 background and phenotyped them in two N conditions from 30 days after planting to full maturity. We extracted time-series plant growth traits from these edited lines as well as the wild type (i.e., TX430), such as plant height, plant width, and pixel counts, along with vegetation indices. Statistical analyses suggested the distinct N responses between some of the edited lines and the wild type. These N-responsive edited lines will be tested in replicated field trials and potentially be incorporated into the breeding protocol for N-resilient sorghum development.
Maize (Zea mays ssp. Mays) is one of the most essential cereal crops in the world. As climate changes, breeding for high nitrogen-use efficiency maize genetic materials without sacrificing the yield becomes more urgent than anytime before. Image-based high-throughput phenotyping, functioning as a key element in plant breeding efforts, is critically important and has the potential to relieve difficulties in phenotypic scoring on breeding pipelines. Numberless studies using RGB images related to biomass and agronomically important traits have mostly focused on above-ground traits. The belowground root-related traits, however, have not been intensively studied. The objective of this study is to investigate the root architecture and phenotypic properties (number of aerial roots, stem diameter, internode length, and fresh root weight) of roots of the hybrid and inbred maize lines grown under low and high nitrogen conditions. In this study, a collection of BGEM lines (n = 304 inbred and n = 197 hybrids) were planted on the field in low and high nitrogen conditions. The root samples of n = 2,100 plants were collected, and the soil around the roots was washed out for automated image-based phenotyping. The roots were imaged with the completely automated conveyor belt LemnaTec system. A high variation in root structure, stem diameter, number of aerial roots, and internode length was observed among the genotypes. Our root phenotyping pipeline and traits extracted from these images will enhance root biology and facilitate breeding for below-ground root traits.
Advances in phenotyping tools, genomic methodologies, and analytics strategies provide new tools to assess germplasm merit; however, more work is needed to integrate these systems into modern plant breeding approaches. The objective of this work is to integrate genomics and phenomics for yield prediction in maize. A panel of 830 temperate and tropical inbred lines were evaluated for their testcross performance in 2018, and a subset of 400 testcross hybrids were evaluated in 2021 and 2022. These experiments were performed in West Lafayette, IN in a randomized complete block design with two replications. Remote sensing data was collected on a near weekly basis throughout each growing season for RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data and grain yield was harvested with a plot combine. Remote sensing traits extracted include canopy cover, plot volume, plant height, and NDVI. A GBLUP genomic prediction model was used to estimate yield performance in 2018 using data collected in 2021 and 2022. Remote sensing traits were estimated at regular intervals throughout each growing season using random regression modelling. Grain yield was estimated using the genomic estimated yield and the remote sensing traits in a machine learning model. Preliminary results indicate remote sensing can improve prediction accuracy of grain yield compared to genomic prediction alone even with data only collected before flowering. Improved prediction accuracy could benefit hybrid selection, increase genetic gain, and reduce cost in a breeding program.
Hyperspectral imaging is a promising method to predict traits in a high-throughput manner with the potential to unlock quantitative genetic studies. Researchers have successfully modeled physiological traits such as vegetative Nitrogen content, but scope of methodology and lack of truly novel testing data hinder large scale trust in the process. Here, I explore the ability to model leaf Nitrogen content from hyperspectral reflectance data collected with a LeafSpec imaging device on 22 maize hybrids. Three broad strategies based on different input feature sets are undertaken. Strategy one mines data for the most informative hyperspectral channels and then constructs a normalized index similar to NDVI as input features. Strategy two considers all 364 channels of hyperspectral data and makes predictions using various machine learning techniques; partial least squares regression(PLSR), random forest regression, and a feed-forward neural net regression. Strategy three aims to take advantage of the spatial distribution of hyperspectral data on the leaf surface by training a convolutional neural net(CNN). A normalized visual index constructed from bands most correlated with nutrient content out-performed established NDVI. PLSR was the most accurate algorithm, followed by feed-forward neural net and then CNN, based on coefficient of determination score. PLSR is well established as a robust method for hyperspectral prediction which is further evidenced by this study. This is one of the first applications of CNN for hyperspectral data. Despite not being the most accurate algorithm there remains room for hyper-parameter optimization.
Critical factors that determine crop yields are located underground, making them difficult to analyze. Traditionally, these factors have been measured by growing plants in clear media and measuring traits with visible imaging. Modern phenomics technologies use one or several imaging modalities to capture traits that reflect plant physiology or performance. Analytical techniques for plant phenomics are a crucial part of approaches to achieving desirable agronomic and biological traits. Advances in sensor technologies have paved the way for faster and more efficient plant phenotyping, with methods adapted from disciplines like high-resolution 3D X-Ray computed tomography (CT). A crucial step in their analysis is segmentation-the identification and classification of the scan's voxels as "root" or "non-root". Unlike roots in transparent mediums, roots in non-transparent mediums are difficult to segment from their surrounding materials as root and non-root voxels have overlapping CT values. The challenge we address is the development of neural-driven approaches for volumetric semantic segmentation of plant roots in 3D CT scans, and discuss subsequent trait extraction methods that enable the quantification of root systems and their traits in several agriculturally
An organism's phenome results from expression of its genome (nature) under certain environment and management effects (nurture) and interactions between these factors, as well as measurement error. For over 30 years, DNA sequencing and genomics tools advanced to where it's now feasible to saturate genomes of segregating individuals, such that polymorphisms at nearly any position can be determined from other known positions. This is due to structure, linkage disequilibrium (LD), or linkage and is a powerful tool for genomic prediction and investigating biological phenomena. In contrast, most phenomics to date focuses on automating previously known "traits" as measurable and interpretable phenotypes; akin to focusing on measuring a single DNA marker rather than measuring an entire saturated genome. Viewing phenomics as a platform for discovery, similar to genomics, opens new methods for capturing phenomena in nature and nurture. Saturating a phenome would mean that an individual's fitness, performance, responses to environment and/or specific phenotypes could be accurately predicted in untested environments. To date, our experience with phenomic prediction for cumulative, complex phenotypes such as grain yield suggests it's possible to predict organismal performance in untested environments, possibly better than genomic methods despite less advanced tools and data. Factors limiting to saturating a phenome are evaluating enough individuals and environments, but more importantly, tools and methods to extract or "sequence" more phenomic features. Successfully saturating phenomes will impact every aspect of science and society, in biological disciplines from germplasm curators, physiologists to breeders, to education, the courtroom and policy.
Plant reproduction is sensitive to heat stress. Pollen tube growth can be accelerated or arrested by high temperatures, leading to unstable tubes, failed sperm cell delivery, and ultimately crop yield loss. Pollen growth dynamics have historically been observed on the scale of individual pollen grains, but there are only a few studies surveying pollen populations across genotypes and environmental conditions. Here we describe a phenotyping system that quantifies tomato pollen characteristics on a large scale and under varied heat stress conditions. In this system, we combined high-throughput bright-field microscopy with automated object detection and tracking to investigate the lives of growing pollen tubes. We used this method to survey pollen from a diverse panel of 220 tomato and close wild relative accessions under different temperatures. This method can be readily adapted to pollen from difference species, providing a rapid way to characterize heat stress responses and molecular functions in flowering plants.
This study evaluates a hyperspectral imaging (HSI) technique to identify herbicide-resistant kochia (Bassia scoparia) biotypes to support weed management in cropping systems. The experiment was conducted under controlled-environment where glyphosate was applied to six different kochia populations. For each population (72 cell tray of plants), half of the plants were sprayed with Glyphosate 900 g ae ha-1 , while the other half remained an untreated control. Hyperspectral images were acquired over five time points spanning from glyphosate treatment to 15 days after treatment (DAT) using a proximal HSI system (Specim-IQ) with 204 spectral bands from 397nm to 1003nm. The average reflectances were extracted from plants that were characterized as glyphosate-resistant or-susceptible. We first analyzed the temporal variations of the spectra with and without the application of herbicide. The spectral profile exploits the advantages of temporal features in biotype discrimination. Random forest algorithms were used to classify the glyphosate-resistant and-susceptible populations, by using reflectance at optimal wavelengths (near-infrared) and various vegetation indices with high correlations with visual ratings. Based on the classification accuracy, the most important wavebands and vegetation indices were determined to classify the weed biotypes. Preliminary results show that: 1) For the untreated plants, the reflectance at red-edge to near-infrared reached the highest level on 8 DAT, revealing the highest chlorophyll content in the leaves. Then, the reflectance declined until 15 DAT. 2) In contrast, strong effects of glyphosate were captured on 8 DAT for the three herbicide-susceptible populations. For the three glyphosate-resistant populations, reflectance at red-edge to near-infrared did not increase from 1 to 8 DAT, which was opposite of the controlled plants.
Irrigation of crops accounts for a significant portion of fresh water consumption. In order to utilize this resource more efficiently, it is necessary to engineer crops that can more efficiently use water. Water use efficiency, defined as the ratio of plant growth to water used, is a complex property of plants affected by many different factors. Despite this complexity, genetic variability has been able to be identified in a number of different crops. The C4 model species Setaria viridis remains under-studied in this regard and consequently we sought to identify promising genetic loci contributing to variation in water use efficiency. In order to accomplish this goal we leveraged the high-throughput phenotyping platform at the Donald Danforth Plant Science center to grow S. viridis in well-watered and water-limited conditions. This automated system enables strict control of watering regimes as well as measures of plant traits extracted from photographs using computer vision. Combining these two sets of data allows for direct measurement of whole-plant water-use efficiency on a daily basis which was used as a response variable in a genome wide association study. Significant associations were found for water-use efficiency and related traits. These loci were then prioritized further by pooling information across each day of an experiment and across multiple experiments to zero in on the most likely locations of genes responsible for driving water-use efficiency in S. viridis.
ORCiD: [ORCiD of presenting author] Jaebum Park [0000-0001-6459-909X] AND/OR Max Feldman [0000-0002-5415-4326]Tuber size and shape, colorimetric characteristics of tuber skin and flesh, and tuber defect susceptibility are all factors that influence the adoption of potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is limited by our inability to precisely measure these features on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we have developed a low-cost, semi-automated workflow to capture data and quantify each of these characteristics using machine vision. This workflow was applied to assess the phenotypic variation present within 189 F1 progeny of the A08241 breeding population and map the genetic basis of tuber characteristics. Several medium-to-large effect, quantitative trait loci (QTL) were found to be associated with different measurements of tuber shape. These results indicate that quantitative measurements acquired using machine vision methods are reliable, heritable, and can be used to map and select upon multiple traits simultaneously in structured potato breeding populations.
Cover crops, plants grown during fallow periods between cash crops, are a promising solution to mitigating soil degradation induced by conventional agricultural practices and improving soil health. Cover crops can provide several beneficial ecosystem functions, such as soil structure remediation, soil microbial diversification, and nutrient recycling, depending on the plant species. Interactions between plant roots and the surrounding soil are key to the plant's ability to perform their ecosystem functions. The lack of data on cover crop roots inhibits our understanding of cover crop phenotype-ecosystem function relationships. We combine aboveground and belowground phenotyping measurements with physicochemical soil measurements to evaluate the field performance of 19 different plant species in monocultures and polycultures as winter cover crops in Missouri. Canopy cover imaging reveals significant differences in winter hardiness and weed suppression among cover crop varieties. Root biomass and root length density measured at depths up to 1 meter indicate differences in rooting behavior between cultivars suggesting the ability to breed cover crop varieties with improved root system architecture. I will also highlight our collaborative efforts utilizing remote sensing technologies (aerial RGB and hyperspectral imaging) to model carbon and nitrogen cycling in cover crop systems at a field scale. Finally, we have begun to characterize 3D root system architecture traits at the seedling stage using a gel-imaging system. Better understanding of cover crop rooting behavior will allow us to breed varieties with enhanced performance of beneficial ecosystem functions for sustainable agricultural systems.
Unmanned aerial vehicle (UAV)-based imagery has become widely used in collecting agronomic traits, enabling a much greater volume of data to be generated in a time-series manner. As one of the cutting-edge imagery analysis tools, machine learning-based object detection provides automated techniques to analyze these imagery data. In our previous study, UAVs have been used to collect aerial photography for field trials of 233 diverse inbred lines, grown under different nitrogen treatments. Images were collected during different plant developmental stages throughout the growing season. This dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season. To improve detection accuracy, we have developed an image segmentation method to remove non-tassel pixels and then feed these filtered images into machine learning algorithms. As a result, our method showed a significant improvement in the accuracy of maize tassel detection. This method can be used in future research to produce time-series counts of tassels at the plot level, and will allow for accurate estimates of flowering-related traits, such as the earliest detected flowering date and the duration of each plot's flowering period. This phenotypic data and the trait-associated genes provide new opportunities for crop improvement and to facilitate future plant breeding.