In Asia, cassava (Manihot esculenta) is cultivated by more than 8 million farmers, driving the rural economy of many countries. The International Center for Tropical Agriculture (CIAT), in partnership with national agricultural research institutes (NARIs), instigated breeding and agronomic research in Asia, 1983. The breeding program has successfully released high-yielding cultivars resulting in an average yield increase from 13.0 t ha-1 in 1996 to 21.3 t ha-1 in 2016, with significant economic benefits. Following the success in increasing yields, cassava breeding has turned its focus to higher-value traits, such as waxy cassava, to reach new market niches. More recently, building resistance to invasive pests and diseases has become a top priority due to the emergent threat of cassava mosaic disease (CMD). The agronomic research involves driving profitability with advanced technologies focusing on better agronomic management practices thereby maintaining sustainable production systems. Remote sensing technologies are being tested for trait discovery and large-scale field evaluation of cassava. In summary, cassava breeding in Asia is driven by a combination of food and market demand with technological innovations to increase the productivity. Further, exploration in the potential of data-driven agriculture is needed to empower researchers and producers for sustainable advancement.

Cassava {Manihot esculenta Crantz) staroh is traded in international markets more than starch from any other source. The starch industry requires cassava cultivars with novel starch characteristics for commercial exploitation. A natural source of waxy (amylose-free) cassava starch, clone AM 206-5, was identified at the International Center for Tropical Agriculture (CIAT). The granule-bound starch synthase I {GBSSl) gene is related to the waxy starch trait. The objectives of this study were to introgress the waxy starch mutation into Thailand's cassava breeding populations, to analyze the inheritance of waxy starch in cassava, and to develop molecular markers for this trait. AM 206-5 {wxwx) was crossed with several elite oultivars {WxWx) to transfer the wx alíele. Unrelated "F/' {Wxwx) genotypes were crossed among themselves to produce a "Fg" segregating population with negligible levels of inbreeding depression. The waxy starch phenotype was recovered in 25.7% ofthe 11,192 genotypes tested. This result provides further evidence that cassava is a functional diploid and that only one copy of GBSSl is present in cassava. A full-length genomic DNA sequence of GBSSl was isolated and characterized from C8, a waxy starch "f^" clone derived from AM 206-5. We exploited these sequence differences to develop two diagnostic single-nucleotideamplified polymorphism (SNAP) markers to differentiate homozygous waxy {wxwx) from the heterozygous {Wxwx) and homozygous {WxWx) nonwaxy genotypes.

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Phenotypic analysis of cassava root crowns (CRCs) so far has been limited to visual inspection and very few measurements due to its laborious process in the field. Here, we developed a platform for acquiring 3D CRC models using close-range photogrammetry for phenotypic analysis. The state of the art is a low cost and easy to set up 3D acquisition requiring only a background sheet, a reference object and a camera, compatible with field experiments in remote areas. We tested different software with CRC samples, and Agisoft and Blender were the most suitable software for generating high-quality 3D models and data analysis, respectively. We optimized the workflow by testing different numbers of images for 3D reconstruction and found that a minimum of 25 images per CRC can provide high quality 3D models. Up to ten traits, including 3D crown volumes, 3D crown surface, root density, surface-to-volume ratio, root numbers, root angle, crown diameter, cylinder soil volume, CRC compactness and root length can be extracted providing novel parameters for studying cassava storage roots. We applied this platform to partial-inbred cassava populations and demonstrated that our platform provides reliable 3D CRC modelling for phenotypic analysis, analysis of genetic variances and supporting breeding selection.