Nowadays analyzing the phenotype is frequently slower and more expensive than genomics due to the difficulties of measuring plant behavior at different levels and under different environements. Thus phenotyping becomes the limiting factor for plant biology and crop improvement. Our knowledge on the link between genotype and phenotype is currently hampered by insufficient capacity of the plant science community to analyze the existing genetic resources for their interaction with the environment. Advances in developing plant phenotyping methods and tools are therefore essential for success in characterizing shoot and root phenes to design next generation crops and forages as key components for climate-smart or eco-efficient agriculture.
Constraints in field phenotyping capability limit our ability to dissect the genetics of quantitative traits, particularly those related to yield, biotic and abiotic stress tolerance (e.g., yield potential, disease and insect resistance, drought, heat and water logging tolerance, and nutrient efficiency, etc.) and mitigation of climate change (increasing soil carbon accumulation and reducing methane and nitrous oxide emissions). The development of effective field-based phenomics platforms remains to be a bottleneck for future advances in genetic gain for yield and nutritional quality. However, progress in remote sensing technology and high-performance computing are paving the way.
The CIAT field Phenomics platform at CIAT-HQ is a state-of-the-art, high-tech facility comprised of automated rainout shelters (for drought screening) and low nitrogen field plots (for Nitrogen use efficiency screening) integrated with multi spectral imaging and Terrestrial Laser Scanning (TLS) system mounted on phenotowers, roof of rainout shelters and unmanned aerial vehicles (UAV). This automated, high-throughput platform allows repeated non-destructive image capture and multi-parametric analysis of small to medium sized field plots at multiple time points. CIAT phenotyping platform is also developing the capacity to estimate root yield in cassava using Ground Penetrating Radar (GPR) technology. The mounting of multi-spectral camera to a drone (UAV) can potentially harness the full capability of proximal sensing in a reliable, flexible, and efficient system that operates spatially at small to bigger plots. Combining this approach with environmental characterization as (Climate, soil and management status of the crop), with GPS positioning to spatially locate the proximal sensing data and with automated image analysis thus appears capable of delivering a robust field based phenomics platform.
We are also developing CIAT-Pheno-i, a scalable analytical framework, for robust aerial image processing especially breeder’s field focus. CIAT-Pheno-i handles different image sources (RGB, Multispectral and thermal) and satellite images helps to organize phenotypic data by retaining the metadata from the input in the result data set.