Current trends in AI tools development in plant biology

Keywords: AI, bioinformatics, plant biology, YOLO, food, phenotyping

TL;DR: AI in plant biology

Abstract: AI tools have revolutionized plant biology and agrobiology by enabling high-throughput analysis of complex biological data, from genomes to phenotypes, supporting precision breeding and sustainable agriculture. We highlight key applications in bioimage analysis using YOLO algorithms, crop plant genome analysis, and computational high-throughput phenotyping tools, discussing recent advancements. Classical genetic selection demands long time and labor-intensive opening way to novel computational modeling. Modern omics technologies, high-throughput genomics, high-throughput plant phenotyping, development of remote sensing devices on-farm provided vast amount of data available in databases. Such platforms and associated data generation have contributed to a booming AI industry in agriculture. Computer methods for plant genome data analysis oriented on stress resistance and crop yield are rely on AI approaches including pattern recognition, neural networks and LLMs. As the example of AI tool development for crop bioinformatics, we present collaborative project between Russia and China “Smart Crop - Cognitive Platform for Reconstruction, Visualization and Analysis of Stress Response Networks based on ANDSystem and Multiomics in Rice and Wheat”. The study of molecular genetic mechanisms of plant resistance to unfavorable biotic and abiotic factors (high or low temperature, drought, salinity, soil diseases, pathogens and pests) requires the study of the functioning of entire molecular genetic systems, including complex signaling, regulatory, transport and metabolic pathways. Finally, we discuss challenges and current trends of AI applications in computational plant biology.

Submission Number: 36