AI Technologies for Crop Breeding

AI Technologies for Crop Breeding by Jen-Tsung Chen offers a forward-looking guide to how artificial intelligence is reshaping plant improvement worldwide. Whether you’re a breeder in North America, a researcher in Europe, or an agronomist supporting smallholders in Asia and Africa, this book delivers practical methods and real-world case studies to turn data into higher-yielding, resilient crops.

Start with a clear overview of machine learning, deep learning, and predictive modeling applied to genomics and phenomics. The author explains genomic selection, marker-assisted strategies, high-throughput phenotyping, and remote sensing with clarity—bridging theory and field-ready practice. Rich examples show how UAV imagery, satellite data, and automated trait measurement accelerate selection cycles and reduce risk under changing climates.

Beyond techniques, the book emphasizes implementation: designing trials, managing noisy data from diverse environments, and integrating AI workflows into breeding pipelines for public and private programs. It highlights cost-effective approaches for resource-limited settings and scalability for commercial operations, making it relevant across geographies and farm sizes.

For breeders, data scientists, agritech entrepreneurs, and policy-makers seeking actionable insight, this volume is both a reference and a roadmap. It explains complex algorithms in accessible language and demonstrates measurable benefits—shorter development time, improved predictive accuracy, and better adaptation to local conditions.

Order this essential resource to modernize your breeding program and harness AI’s potential to feed a growing world. Explore practical strategies and start translating data into superior varieties today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.