CGIAR data scientists join hands to better machine learning in agriculture – ICRISAT

Data scientists used the opportunity to learn advanced trends in artificial intelligence, machine learning and deep learning methods in genomic prediction models. A deeper understanding of advanced trends in artificial intelligence (AI), machine learning (ML) and deep learning methods in genomic prediction models is critical to the success of smallholder agriculture. AI and ML algorithms are now being used to reduce risks in agriculture while also making it possible to forecast pest and disease outbreaks and alert farmers in advance. The annual collaborative workshop for Bioinformatics & Biometrics Community of Practices (CoP) under Excellence in Breeding (EiB) Platform Module 5, held in July in Montpellier, France, discussed the untapped potential of deep learning methods to make a significant impact on farming. With the theme: "Artificial Intelligence & Machine Learning with Genomic Selection Use Cases", the workshop served as a platform for data scientists across CGIAR institutions to explore using advanced agricultural research ML algorithms for genomics including prediction of plant phenotype, image identification, disease identification, and annotation of DNA sequences.