DEVELOPING FIELD-BASED HIGH-THROUGHPUT PHENOTYPING FOR COFFEE YIELD, PHYSIOLOGICAL PERFORMANCE AND DISEASE RESISTANCE

Phenomics is the quantification of plant growth, performance and composition to efficiently connect genomics to plant function and agricultural outputs. Modern phenomics applied to plant breeding includes sensing and evaluation platforms that can be affordably deployed to dramatically improve throughput. Phenomic prediction can allow for more accurate direct selection. It provides for objective evaluation of traits and crop characteristics, at higher frequency and for larger populations across a broader range of environments than traditional breeding selection methods and genomic prediction. Hyperspectral reflectance observations have gained significant momentum as a key tool for phenomic assessment due to dense and diverse information content in highly resolved spectra (order of 1-5 nm per resolved spectral band), allowing for the identification of a variety of traits related to pigment and nutrient contents, leaf structure and moisture content, disease incidence or susceptibility, among others. The overarching goal of this project is to bring the power of hyperspectral reflectance phenotyping to coffee assessment and breeding through the development of coffee-specific algorithms related to gas exchange parameters, foliar nutrient profiles, yield and coffee rust incidence and resilience.There have been relatively few efforts to integrate high-throughput phenomics into coffee selection and breeding. Most applications have been focused on seed composition and origin classification. There have not been any efforts to deploy phenomic technologies to increase coffee seed yield directly, which is the main factor affecting farmer profitability. Assessing plant photosynthetic capacity offers one path to enhancing plant productivity and seed yield. Developing a high-throughput algorithm to quantify coffee photosynthetic capacity, across growth habit and environmental variation, from rapid hyper-spectral reflectance measurements is one of the primary goals of this project, motivated by the fact that model calibration for this approach has not been found transferable between species.Another primary goal of this project is the development of a set of algorithms relating costly and time-consuming leaf nutrient composition observations with rapid hyperspectral reflectance measurements. Leaf nutrient composition is not only important for overall surveys of plant and soil health, but of importance to study the possible confounding interactions of foliar nutrient contents with photosynthetic capacity and overall yield. For example, in coffee high leaf phosphorus content has been associated with high photosynthetic capacity under drought.Another important goal of this project is the development of hyperspectral reflectance algorithms related to the susceptibility and incidence of coffee leaf rust. Coffee leaf rust is the most important pathogen in coffee, reducing both yield and quality. Developing reflectance methods to evaluate and predict the susceptibility of coffee plants to rust damage would be useful for both surveillance of coffee plantations as well as the early evaluation of germplasm with resistance to the pathogen. In other crops and other species of leaf rust, reflectance-based methods have been demonstrated to predict disease severity prior to expert evaluation. In coffee, leaf rust detection through spectroscopy has been focused on a few vegetation indices, not taking advantage of the information in the full visible through shortwave infrared spectrum available through many of our modern field spectrometers. Our approach will utilize the full information in high spectral resolution reflectance spectroscopy measurements for leaf rust incidence detection.