precision-horticulture
Precision horticulture applies sensor networks, imaging technologies, and data analytics to monitor and manage individual plants or crops with fine-scale accuracy, optimizing inputs like water, nutrients, and light based on real-time plant needs. This approach matters for plant science because it enables researchers to detect subtle physiological responses to environmental conditions, accelerating the study of plant stress responses, growth patterns, and resource-use efficiency. By integrating technology with cultivation practices, precision horticulture supports more sustainable and productive growing systems while generating detailed datasets for advancing plant biology research.
open_in_new WikipediaOpenAlex · 2045-12-10
This chapter introduces how artificial intelligence — software that learns from data to perform tasks once requiring human judgment — is being applied across plant sciences, from crop monitoring to disease detection.
AI enables machines to autonomously perform tasks in plant sciences that previously required human expertise and judgment.
Machine learning algorithms — the core engine of AI — train on large volumes of input data to recognize patterns and make decisions.
AI applications in plant sciences span a diverse range of uses, suggesting broad potential across crop management, disease diagnosis, and botanical research.