Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability...
The swiftness of artificial intelligence (AI) progress in plant science begets relevant ethical questions with significant scientific and societal implications. Embracing a principled approach to regulation, ethics review and monitoring, and human-ce...
Understanding protein function by deciphering 3D structure has distinct limitations. A recent study by Huang et al. used AlphaFold2, an artificial intelligence (AI) protein-folding prediction model, to predict and classify deaminase proteins based on...
The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and producti...
Large language models (LLMs) will benefit science by accelerating task performance. We explored whether answers generated by ChatGPT (generative pretrained transformer) to questions of biology are sufficiently diverse. 'Plant awareness' in ChatGPT an...
Artificial intelligence (AI) is advancing rapidly and continually evolving in various fields. Recently, the release of ChatGPT has sparked significant public interest. In this study, we revisit the '100 Important Questions Facing Plant Science' by le...
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large d...
Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stres...
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access dataset suitable for ML in image-based plant phenotyping suitable fo...