Image-based machine learning models for customized soil moisture management.

ABSTRACT: Crop growth can vary even under the same cultivation conditions, highlighting the limitations of conventional smart farming systems that apply uniform treatments to all crops. These average-based approaches often overlook individual plant needs shaped by microenvironments and physiological differences, resulting in inefficient resource use and reduced yields. While crop-specific management is important for improving productivity, there is a lack of non-invasive methods to monitor soil conditions at the individual plant level. This study presents an AI-based system that combines soil sensors and image analysis to support customized moisture management. Transplanted wild-simulated ginseng was used as a model crop. RGB images of the soil surface were collected with sensor data from different

SUBMITTER: Kim Y 

PROVIDER: S-EPMC12912613 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature