Field-Based Water and Fertilizer Decision-Making Using Crop Sensing Data and Economic Records

Abstract

Water and fertilizer management in facility and precision agriculture increasingly depends on images, soil sensors, weather records, management logs, and economic data rather than on empirical operation alone. A cross-modal decision framework is developed to align crop visual traits with soil water–salt dynamics, meteorological variation, and management operations. Image preprocessing, abnormal sensor screening, missing value interpolation, temporal resampling, multi-window alignment, and temporal augmentation are used to form comparable inputs before multimodal fusion. Using plot-blocked evaluation that keeps complete plot trajectories together, AgriWFD-RLNet obtains 93.46% status-recognition accuracy and an R2 of 0.923 for yield–resource–economic response prediction. In offline policy evaluation against the logged conventional management reference, it estimates 16.92% water saving, 14.37% fertilizer saving, and 15.84% Net Return improvement while maintaining 94.68% yield stability and 96.72% safety compliance. These decision outcomes are model-based estimates from the 2024 Wuyuan dataset rather than outcomes of a prospective field deployment.