{"product_id":"machine-learning-for-plant-biology-isbn-9781394329618","title":"Machine Learning for Plant Biology","description":"\u003cp\u003e\u003cb\u003eA comprehensive and current summary of machine learning-based strategies for constructing digital plant biology\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning for Plant Biology\u003c\/i\u003e provides a comprehensive summary of the latest developments in machine learning (ML) technologies, emphasizing their role in analyzing complex biological networks of plants and in modeling the responses of major crops to biotic and abiotic stresses. The combinatorial strategies discussed in this book enable readers to further their understanding of plant biology, stress physiology, and protection. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning for Plant Biology\u003c\/i\u003e includes information on: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eIntelligent breeding for stress-resistant and high-yield crops, contributing to sustainable agriculture, the Sustainable Development Goals (SDGs), and the Paris Agreement\u003c\/li\u003e \u003cli\u003eInteractions between plants, pathogens, and environmental stresses through omics approaches, functional genomics, genome editing, and high-throughput technologies\u003c\/li\u003e \u003cli\u003eState-of-the-art AI tools, including machine and deep learning models, as well as generative AI\u003c\/li\u003e \u003cli\u003eApplications include species identification, systems biology, functional genomics, genomic selection, phenotyping, synthetic biology, spatial omics, plant disease diagnosis and protection, and plant secondary metabolism\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Plant Biology\u003c\/i\u003e is an essential reference on the subject for scientists, plant biologists, crop breeders, and students interested in the development of sustainable agriculture in the face of a changing global climate. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTable of contents\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003col\u003e \u003cli\u003eEdge-based machine learning for computer vision in smart plant biology imaging\u003c\/li\u003e \u003cli\u003eMachine Learning for Studying Plant Evolutionary Developmental Biology\u003c\/li\u003e \u003cli\u003eMachine Learning for Plant High-Throughput Phenotyping\u003c\/li\u003e \u003cli\u003eMachine Learning for Studying Plant Secondary Metabolites\u003c\/li\u003e \u003cli\u003eMachine Learning for Plant Ecological Research\u003c\/li\u003e \u003cli\u003eMachine Learning for Modelling Plant Abiotic Stress Responses\u003c\/li\u003e \u003cli\u003eMachine Learning for Modelling Plant-Pathogen Interactions\u003c\/li\u003e \u003cli\u003eMachine Learning-Enhanced Plant Disease Detection and Management\u003c\/li\u003e \u003cli\u003eMachine Learning for Analysing and Integrating Multiple Omics\u003c\/li\u003e \u003cli\u003eMachine Learning for Plant Single-Cell RNA Sequencing\u003c\/li\u003e \u003cli\u003eMachine Learning for Plant Genomic Prediction\u003c\/li\u003e \u003cli\u003eMachine Learning-Assisted Plant Systems Biology\u003c\/li\u003e \u003cli\u003eMachine learning-driven precision plant breeding\u003c\/li\u003e \u003cli\u003eMachine Learning-Driven Smart Agriculture\u003c\/li\u003e \u003cli\u003ePlant Leaf Disease Detection and Classification Using Convolutional Neural Networks\u003c\/li\u003e \u003cli\u003eThe Future Farming: Machine Learning and Crop Health\u003c\/li\u003e \u003cli\u003eSocial Impact of Machine Learning on agricultural Communities\u003c\/li\u003e \u003cli\u003eEthical and regulatory considerations of machine learning in modern agriculture\u003c\/li\u003e \u003c\/ol\u003e  \u003cp\u003e\u003cb\u003eJen-Tsung Chen\u003c\/b\u003e is a Professor of Cell Biology at the Department of Life Sciences, National University of Kaohsiung, Taiwan, where he teaches courses on cell biology, genomics, proteomics, plant physiology, and plant biotechnology. His research interests include bioactive compounds, chromatography techniques, plant molecular biology, plant biotechnology, bioinformatics, and systems pharmacology. In 2023 and 2024, Elsevier and Stanford University recognized Dr. Chen as one of the “World's Top 2% Scientists”. In 2025, Dr. Chen received the \"Springer Nature Editorial Contribution Award\" for his contributions to Plant Methods.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989548712165,"sku":"NP9781394329618","price":178.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394329618.jpg?v=1761784552","url":"https:\/\/k12savings.com\/products\/machine-learning-for-plant-biology-isbn-9781394329618","provider":"K12savings","version":"1.0","type":"link"}