CTCRI - CENTRAL TUBER CROPS RESEARCH INSTITUTE

1. On-farm testing and frontline demonstrations to evaluate the location-specific applicability and for showcasing the production potential of  tuber crop technologies in farmers' fields.

2. Capacity building of farmers, extension personnel and other stakeholders by updating their knowledge and skills in advanced tuber crop        technologies.

3. Farm advisories and effective use of media to promote tuber crop varieties and technologies among farmers and stakeholders.

4. Gender analysis to empower women in tuber crops farming.

5.Impact assessment and value chain analysis of tuber crops for strengthening tuber crops sector.

6. ICT and smart farming technologies

• A System and a Method for Automated Fertigation of Crops (SMAFC): it is a device for fertigation of crops for precise and automated application of nutrients and water to crops to reduce yield gaps and optimize crop yield potential as per the advisory of e-Crop

• IoT device: Electronic Crop (e-Crop) is an AI enabled IoT device that simulates crop growth in response to weather and soil parameters and generates agro-advisory that is sent to the farmer’s mobile by SMS.

• Crop growth simulation models: EFYSIM- an elephant foot yam growth model, SPOTCOMS- sweet potato growth model; SIMCAS-a cassava growth model; MADHURAM-the world's first sweet potato growth model. 

• Decision Support System: Sree Visakham cassava expert system; Tuber crops online marketing system, OUSHADHAM for disease and pest diagnostic system for tuber crops, Cassava protector, Tuber Information Cafe, CASSNUM 1.1 for nutrient management of cassava.

• Mobile Apps: Krishi Krithya for e-Crop based smart farming; Variety Finding Tool (VFT) cassava and VFT taro; TuberGuru App for information of tropical tuber crops; Sree Poshini for site specific nutrient management based fertilizer recommendations.

• Database/Information system: TUBERTECH on the ICAR-CTCRI technologies; TUBERHELP on information system of tuber crops. 

• Developed web based early warning system for mealy bug, Sree Visakham cassava expert system 

7.Enhancing tuber crops research and development through big data analytics, statistical and bioinformatics tools.

• A web application was developed for carrying out basic statistical data analysis and data visualization specifically tailored for Agricultural Research, Agrianalytics@R can be accessed at https://sreejyothi.shinyapps.io/agrianalyticsr/

• Developed an Interactive database of genomic variations in cassava, CasGVD. Genomic variations (SNPs and INDELs) compiled from whole genome analysis of two genotypes, Sree Kaveri and 9S-127. The integrative genome viewer (IGV) was integrated with the genomic variant database, which helps in chromosomal location wise retrieval of genomic variants in cassava.

• Methodology developed for comparative and functional genomics analysis and reconstructed the starch biosynthesis and carotenoid biosynthesis pathways in cassava.

• Developed an R-package for computing Soil Quality Index (SQI) by integrating ANOVA, Principal Component Analysis and computation of SQI.

• Machine learning models were developed for the prediction of plant pathogen protein-protein interaction for colocasia-Phytophthora interaction using the protein sequence information on plant and pathogen. An interactive web application for prediction of interacting proteins using RShiny was developed which can select the protein features and the methods for prediction of plant host pathogen interaction.

• MetaQTL associated with cassava mosaic disease resistance and cassava brown streak disease resistance were identified using meta-analysis of Quantitative Trait Loci (QTL) in cassava for biotic stress resistance.

• An R tool was developed for identifying optimal number of clusters, clustering using K means, hierarchical clustering, computing intra and inter cluster distances at optimal number of clusters.

• SAS macro has been developed in SAS which can be used along with PROC glm for attaching superscripted alphabets to interaction means in factorial experiments.

• An interactive web-based gene network development tool, RIntGeneNet was developed using R, which facilitates the construction of gene regulatory networks from microarray gene expression data.

• Developed database of SNPs and miRNAs in cassava and elephant foot yam

• A web based interactive database of tuber crops statistics has been developed using ‘R environment for statistical computing’ and Shiny package.

• Developed a new method based on correlated mutation analysis, RMRCM, for predicting protein-protein interaction using regularized multinomial regression. R-code for implementation of the new method RMRCM is available via www.ab.wur.nl/rmrcm.