This paper demonstrates the development of Taiwan Data Cube, TWDC, and also showcases successful applications in crop monitoring and wildfire in Thailand and Paraguay respectively. TWDC is an enhancement version of Open Data Cube, ODC, which is being promoted by the Committee on Earth Observation Satellites, CEOS. TWDC Enhancement includes Imagery Data as well as High-Performance Computing Implementation, Private-Public Cubes Service Framework, and Integrated Cloud Analysis. TWDC contains imagery of Sentinel-l & 2, Landsat, and Formosat-2 & -5.

One of the major considerations in precision agriculture is optimizing fertilization to ensure maximum crop productivity. The principle behind this precision fertilization is to adjust the fertilizer inputs according to the properties of soils and crop stage such that crop yield is maximized and fertilizer loss is minimized. For this, one needs to understand the default soil nutrient status. This paper presents a feasible approach for developing high resolution spatial map (30m) of soil nutrients based on machine learning model with multiple sources covariates from climate, terrain, remote satellite etc. The covariate variables in different scales were chosen based on their association with soil nutrients and converted to construct field scale soil nutrients (pH, OM, EC, N, P & K) mapping models. The nutrients distribution is able to capture the spatial variability with moderate to high accuracy. This research is a methodological contribution to precision agriculture and lays the ground for precise application of fertilizers.

Sugarcane is a high biomass crop that requires large quantities of water for maximum yield. The study aims to estimate daily actual water consumption or crop evapotranspiration ( ETc) at field scales for practical applications with real data, remote sensing (RS) observation using novel methodologies (ML: machine learning and LSM: land surface model). Northeast Thailand is the study area chosen and three Sugarcane growing seasons (2016 to 2019) is the duration of the study. Similarities between the crop coefficient ( Kc) curve and a satellite-derived leaf area index (LAI) showed potential for estimation of Kc maps. A regression model has been developed to establish LAI vs Kc relation and used to derive daily Kc maps. With a view to computing daily reference evapotranspiration ( ETo) driven by weather, RS data, soil texture, land charac-teric etc, a high resolution LSM has been customized. The results shows ETc distribution low at initial and early development stages, while ETc tends to be high during grand growth and yield formation stages. Significant spatiotemporal variation has been observed across fields. Analysis of 19 fields for complete three seasons has been undergone with regard to yield response to water consumption and outcomes confirm standard yield reduction due to water stress. The daily ETc maps aided to demonstrate the variability of crop water use during growing season at field scales. Further, using ETc maps at the field scale in near real time, growers can supply optimal water to maximize yield, leading to water conservation in scale.

In-season yield and commercial cane sugar (CCS) level estimation for sugarcane is critical for fine tuning the irrigation, fertilizer and pesticide schedules during the growing season to increase the yield and CCS and for optimizing the supply-demand logistics. In this paper, we present a case study of estimating in-season, field-level yield and CCS for sugarcane using crop attributes, weather and soil data as features for three corporate owned sugarcane farms in Thailand. We provide a detailed analysis of feature correlation, and accuracy of the yield and CCS estimation models across growing conditions and stages. Using various machine learning techniques to estimate yield and CCS, we obtained minimum normalized root mean square error (NRMSE) with linear regression. The values of minimum NRMSE for yield and CCS estimation are 0.197 and 0.203 respectively.

SUMMARY: Ka-me is a Voronoi image analyzer that allows users to analyze any image with a convex polygonal tessellation or any spatial point distribution by fitting Voronoi polygons and their dual, Delaunay triangulations, to the pattern. The analytical tools include a variety of graph theoretic and geometric tools that summarize the distribution of the numbers of edges per face, areas, perimeters, angles of Delaunay triangle edges (anglograms), Gabriel graphs, nearest neighbor graphs, minimal spanning trees, Ulam trees, Pitteway tests, circumcircles and convexhulls, as well as spatial statistics (Clark-Evans Nearest Neighborhood and Variance to Mean Ratio) and export functions for standard relationships (Lewis's Law, Desch's Law and Aboav-Weaire Law). AVAILABILITY: Ka-me: a Voronoi image analyzer is available as an executable with documentation and sample applications from the BioQUEST Library (http://bioquest.org/downloads/kame_1.0.rar).

Bioinformatics is central to biology education in the 21st century. With the generation of terabytes of data per day, the application of computer-based tools to stored and distributed data is fundamentally changing research and its application to problems in medicine, agriculture, conservation and forensics. In light of this 'information revolution,' undergraduate biology curricula must be redesigned to prepare the next generation of informed citizens as well as those who will pursue careers in the life sciences. The BEDROCK initiative (Bioinformatics Education Dissemination: Reaching Out, Connecting and Knitting together) has fostered an international community of bioinformatics educators. The initiative's goals are to: (i) Identify and support faculty who can take leadership roles in bioinformatics education; (ii) Highlight and distribute innovative approaches to incorporating evolutionary bioinformatics data and techniques throughout undergraduate education; (iii) Establish mechanisms for the broad dissemination of bioinformatics resource materials and teaching models; (iv) Emphasize phylogenetic thinking and problem solving; and (v) Develop and publish new software tools to help students develop and test evolutionary hypotheses. Since 2002, BEDROCK has offered more than 50 faculty workshops around the world, published many resources and supported an environment for developing and sharing bioinformatics education approaches. The BEDROCK initiative builds on the established pedagogical philosophy and academic community of the BioQUEST Curriculum Consortium to assemble the diverse intellectual and human resources required to sustain an international reform effort in undergraduate bioinformatics education.