Recipient Organization
UNIVERSITY OF CALIFORNIA, DAVIS
410 MRAK HALL
DAVIS,CA 95616-8671
Performing Department
Bio and Ag Engineering
Non Technical Summary
Vegetable production in the US is a $17 billion industry, with ten states accounting for 77 percent of all US vegetable sales for fresh market and processing uses: California, Florida, Washington, Idaho, Arizona, Wisconsin, Oregon, Texas, Michigan and North Carolina (USDA-NASS, 2012 Census of Agriculture). The largest crops by acreage are potato, lettuce, sweet corn, watermelon and tomato. Major vegetable crops in the Solanaceae family include potato, tomato, and pepper; this family is the third most economically important food crop plant taxon (solgenomics.net/about/about_solanaceae.pl). Tomato and pepper contribute to human nutrition and food security in the US. Tomato (Solanum lycopersicum) fruits and products made from fruits contribute significantly to the US dietary intake of lycopene, ascorbic acid (vitamin C), and tocopherols (vitamin E); these antioxidants have been reported as possessing cardio-protective and chemo-preventive activities. Pepper fruits are a rich source of carotenoids (provitamin A) and ascorbic acid, and also supply several B vitamins in the human diet. Tomatoes are consumed fresh or processed (as sauce, paste, diced, juice, etc.) and are an excellent source of nutrients for humans. Peppers (Capsicum annuum) are also consumed fresh or as processed products (as sauces, salsa, diced, roasted, dried and ground). Over the past century, numerous methodologies and technologies have been developed and adapted by plant breeders to expedite cultivar development. Recently, genomics technologies, such as DNA-based markers and genome sequencing, have provided breeders the means to select plants with the desired traits based on their genotype, a process known as marker-assisted selection (MAS). Genotyping has become increasingly high-throughput and cost-effective, with major technical advances in genotyping platforms and data management significantly reducing the costs of employing MAS. Despite rapid advances in genomics, in-field trait phenotyping remains a major bottleneck for crop cultivar breeding and wider implementation of MAS and genomics-facilitated breeding. The manual trait phenotyping process is costly, labor- and time-intensive, and often precludes repeated measurements on the same plants, or plots of plants, over time. Cultivar breeding programs require phenotyping of thousands of plants grown in field locations typical for commercial production of the crop. Multiple traits, some of which are genetically complex (e.g., yield, fruit quality, abiotic stress tolerances), must be assessed for each plant, family or line in breeding populations in order to accurately identify and select superior plants or families that can be developed into cultivars. Traits of major importance in both tomato and pepper include fruit yield, color, size and shape. Other key traits in both crops include leaf canopy cover (to prevent fruit sunburn), days to maturity, plant size (architecture), and drought and heat tolerance. Depending on the end uses of the fruits, other traits may be of importance. For example, in processing tomatoes, red fruit that is square-round in shape with high soluble solids (Brix) content are desirable, as is determinant plant habit for mechanical harvesting. In fresh market tomato, desired fruit shapes, sizes and color vary by market class (e.g., beefsteak, plum, cherry, heirlooms, etc.) and the plant architecture may range from determinant bush to indeterminate vine-types that are trellised and repeatedly hand-harvested. In pepper, target fruit traits for breeding depend partly on whether the peppers are sweet (non-pungent) or hot (pungent), and if they are used fresh, dried, ground or in processed products. Sweet pepper pod types include bell, pimento, Cuban and squash, while pungent (hot) pepper pod types include cayenne, jalapeno, serrano, ancho, pasilla, New Mexican and others. Fruit colors in pepper can vary from green to red, purple, yellow and orange. Pod types are distinguished by their shape as well as by color, pungency (heat) level, aroma and/or flavor; these categories are used by the pepper industry to identify the correct pepper for the proper (end) product. Climate change is projected to reduce crop yields and increase the frequency of extreme weather events in major food producing regions including the arid western United States. Efficient breeding of crop cultivars that are productive and resilient in a changing climate is needed to address this major threat to food security. The current labor-, cost- and time-intensive manual methods of evaluating (phenotyping) traits in field-grown crop plants are a major bottleneck in plant breeding that slows progress. Efficient use of genomic tools (including high-throughput genotyping) in breeding cannot be implemented at the required scale and speed without a transformational change in approach to in-field trait phenotyping. Development of new technologies and approaches for efficient high-throughput in-field phenotyping methodologies are urgently needed by plant breeders to achieve rapid development of new crop varieties to meet the threats of climate change and provide food security for a growing population. The developmental pace of in-field phenotyping technology lags behind that of genomics technology. As a consequence, trait phenotyping in the field is a significant bottleneck for efficient breeding of crop cultivars. The development and application of technologies that result in accurate, high-throughput plant phenotyping in the field would serve to accelerate breeding crop cultivars. More recent developments in sensors, machine vision and machine learning techniques, higher resolution digital cameras, massively parallel data processing power and other applicable technologies have now paved the way to enable high-throughput in-field plant phenotyping (HTPP) in the field to benefit food crop breeding programs. In this three-year project we will focus on the development and deployment of automated, smart HTPP systems to accelerate breeding of new and novel vegetable cultivars adaptable to climate change. We will focus on tomato and pepper, important members of the Solanaceae family of vegetable crops (i.e., tomato, pepper, eggplant, potato). Tomato and pepper have similarities in breeding objectives for a number of plant and fruit traits of importance to growers, consumers and processors. Longer term (beyond this project timeframe) we plan to adapt the HTPP technologies and methods that we develop for tomato and pepper to other important food crops.
Animal Health Component
33%
Research Effort Categories
Basic
(N/A)
Applied
33%
Developmental
67%
Goals / Objectives
Long-term goal:Development and deployment of automated, smart high-throughput in-field plant phenotyping (HTPP) systems to accelerate breeding of new and novel vegetable cultivars adaptable to climate change. In this three-year project we will focus on tomato and pepper, important members of the Solanaceae family of vegetable crops (i.e., tomato, pepper, eggplant, potato). Tomato and pepper have similarities in breeding objectives for a number of plant and fruit traits of importance to growers, consumers and processors. Longer term (beyond this project timeframe) we plan to adapt the HTPP technologies and methods that we develop for tomato and pepper to other important food crops.Supporting objectives of the proposed project: Objective 1. Development of automated, high-throughput plant phenotyping (HTPP) technology for predictive phenotyping of fruit and plant traits relevant to breeding tomato and pepper grown using commercial on-farm cultural practices. Objective 2. Performance assessment and validation of automated HTPP predictive phenotype measures using "ground truth" trait data obtained by plant breeders via manual phenotyping of plant and fruit traits in the field. Objective 3. Initiate deployment of automated HTPP in vegetable breeding by field evaluation of segregating breeding lines genetically unique from those evaluated in years 1 and 2 to assess the ability of HTPP to identify superior breeding lines.
Project Methods
Activities for Objective 1: Automated high-throughput plant phenotyping (HTPP) will be achieved by developing a physical research prototype machine and custom software designed to measure breeding trait phenotypes in tomato and pepper that span a range of plant and fruit characteristics, and include traits of importance to vegetable growers, processors and consumers. To address current phenotyping bottlenecks in breeding progress, our approach is to develop a mobile smart HTTP machine (ground-based) that uses non-contact sensing methods to acquire measurement data while the sensor platform is in motion. In this project, trait phenotypes are classified as either exterior or interior, based upon the location relative to the plant canopy envelope where the required information resides. Because some of the important traits require detailed information about interior traits, our proposed approach is to use proximal sensing techniques from a mobile ground vehicle. HTPP measurements will be achieved using a sensor suite consisting of the existing high-resolution digital cameras of the Robotic in-Field Phenotyping System (RFPS) plus new sensor capability for real-time leaf temperature measurement. Raw sensor data will be analyzed using a multifaceted approach. For many exterior plant phenotypes, algorithms developed in the Slaughter lab and elsewhere will be adapted for application to HTPP data for plants in plot-sized groups once canopy closure within the row occurs. Further adaptations of the algorithms will be implemented to run at a higher-throughput on the high performance computing cluster at UC Davis. For some trait phenotypes where no prior research has been conducted, new algorithms will be developed in this project. For interior phenotypes and some whole plant phenotypes a multi-step, machine learning approach based upon stereology techniques will be used. In this case, phenotype estimates will be generated using an expert system multiclassifier approach. Separate predictive models for each interior phenotype and morphological category will be created to predict the trait phenotype across the whole plot. A stereological plant model for the most probable morphological category will then be used to estimate the phenotype based upon visible information accessible to the RFPS.Activities for Objective 2: Breeding materials for field experiments: Tomato: For our experiments in this project, a set of processing tomato breeding lines segregating for tolerance to abiotic stresses (drought & high temperatures), plant traits (e.g., plant size, plant shape, etc.) and fruit traits (color, size, shape, etc.) will be evaluated in replicated field experiments at UC Davis. Genotyped, advanced tomato breeding lines derived from crossing cultivated tomato with wild tomato species with agriculturally desirable traits, including tolerance to abiotic stresses (e.g., drought) and fruit traits (yield, maturity, etc.) will be studied. Pepper: Advanced pepper breeding lines and cultivars, including New Mexican red chile peppers, yellow wax peppers, and bell peppers will be studied. These lines and cultivars represent a range of plant traits, fruit traits and pod types that are important in pepper. Materials selected for tolerance to reduced irrigation water will be included in our field experiments.Field experiment design and methods: For in-field breeding experiments, a split plot experimental design will be used for each crop each year of this project: main plots will be the drip irrigation treatment with three blocks per main plot, and subplots will be the breeding lines, with border rows between irrigation treatments. The two split plot experiments (one for tomato, one for pepper) will be arranged next to each other in the field, such that all the pepper plots are located in one split plot experiment, and all the tomato plots are in the other split plot experiment. This experimental layout will allow each crop to be managed separately for fertilizer, weeds, pests/diseases, etc. using standard commercial practices for each crop. Seedlings will be hand- transplanted into 10-plant plots on 1.52 m center beds, with 0.3 m between plants within a row at UC Davis. In year 3, we will use the same experimental design but with segregating breeding lines that are genetically unique from those evaluated in years 1 and 2 to assess the ability of HTPP to identify superior breeding lines.Trait phenotyping performed by HTPP and manually by breeders: HTPP measurements will be made in the 10-plant plots at regular time intervals to characterize plant and fruit traits across a range of developmental stages and to allow a time-series analysis of the HTPP measurements. A logging weather station will be used in the experimental field to record weather data. Weather data will be combined with leaf temperature distributions and plant size estimates to estimate abiotic stresses levels. In parallel with HTPP, plant and fruit traits will be evaluated manually in the field by plant breeders and graduate students. Manual phenotyping will be done subjectively for some traits with scoring scales that have been used previously in our breeding programs, and for other traits we will develop appropraite objective measures. A sample of 20 fruit from each subplot will be harvested and weighed, and analyzed for size and shape. Fruit yield will be obtained at season end from two plants per subplot: all fruit will be removed, sorted by color/maturity, counted and weighed, and then two plant shoots without fruit will be weighed to estimate fresh weight plant biomass.Trait data analyses and comparisons to HTPP data: Trait data obtained manually by the breeders will be analyzed on a per-crop and per-trait basis with ANOVA for a split plot design. Effects of the two irrigation treatments in the split plot experiment for each crop will be tested per trait. We will compare trait data obtained manually with the HTPP measurements. For directly comparable measurements, an analysis of covariance will be conducted, with morphological category and irrigation treatment as the covariates, to determine the predictive performance of the HTPP measurements. For categorical phenotypes, a discriminant analysis will be conducted to compare the manual phenotype classifications to the HTPP classifications. For some interior traits, a time series analysis of covariance approach will be taken to determine the performance by morphological category. We will also conduct a comparison of the degree of genotype separation by calculating the mean separations and precision using a variance normalized multidimensional distance measure between the genotype means and the within genotype variance for both the manual method and the HTPP method.Activities for Objective 3: Breeding materials, field experiments, trait phenotyping: Tomato and pepper breeding lines that are genetically unique from those used in the first two years will be employed in year 3 of the project. The field experimental design, field methods, traits and phenotyping methods, and data analyses will be the same as those described under Objective 2. A direct comparison of ranking of breeding lines' trait performance as measured manually and with HTPP, will also be conducted. Spearman's rank correlations will be calculated by crop and by trait for the two phenotyping methods to determine the degree of concordance or agreement. For each crop, the trait data will also be evaluated using principle component analysis (PCA) and cluster analysis to evaluate if a similar set of best performing lines for multiple traits are identified by both HTPP and manual phenotyping.