Accepted_test
Eftekhari M.1*, Ma C.2,3, Orlov Y.L.4,5
1 Department of Horticultural Sciences, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
2 State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of
Bioinformatics, College of Life Sciences, 3 Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Shaanxi, China
4 Institute of Cytology and Genetics, SB RAS, Novosibirsk, Russia, and 5Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow, Russia,
* m.eftekhari@modares.ac.ir, chuangma2006@gmail.com, orlov@bionet.nsc.ru
Key words: computational plant biology, AI-driven plant breeding, omics data analysis
In recent years, the field of plant breeding has witnessed a paradigm shift driven by advancements in artificial intelligence (AI) technologies, including machine learning (ML) and deep learning (DL) technologies. These cutting-edge techniques have transformed our understanding of plant biology. From decoding the intricate molecular mechanisms of plant defense to automating disease detection and optimizing nutrient levels, AI is reshaping the landscape of plant breeding. AI-assisted omics techniques offer insights into plant-pathogen interactions and facilitate the identification of stress-responsive genes. We have organized thematic journal issue at Frontiers in Plant Science – Research Topic “Applications of artificial intelligence, machine learning, and deep learning in plant breeding”. We collected research papers on the topic of AI applications in plant biology in areas of sequencing data analysis, image recognition, technology process optimization. Continued investment in AI applications in plant breeding holds the key to unlocking the full potential of agriculture.