WPS-Compliant End to End Interactive Platform for Road-side Field View Crop Identification

High-resolution crop monitoring at the field level remains a significant challenge. Overhead imagery from satellites or UAVs carries high costs, depends on favorable weather, and cannot revisit fields quickly enough to capture time-sensitive changes in plant development. Top-down views also miss the fine detail needed to tell neighboring crops apart or to detect subtle variations within a single field. Ground-level photographs provide the close-up perspective required for accurate parcel-scale identification without special equipment or lengthy field surveys.We built a roadside field view crop classification platform based on OGC Web Processing Service standards, using Flask and PyWPS for modular, location-aware processing. A UNet model enhanced with an attention mechanism classifies maize and soybean from ground photos. Classification results and coordinates feed into an SQLite database with R-Tree spatial indexes, enabling fast geographic queries and easy integration with GIS software. A React-based user interface lets users explore available classifications and upload new photos. A separate React administrator panel provides tools for manual label correction, record inspection, real-time system monitoring, and data download. The end-to-end pipeline automates photo upload, model inference, and spatial indexing. The platform demonstrates the possibility of large-scale crowdsourcing of ground truth by making in-situ data collection as simple as snapping and uploading street-level field photos. It boosts the accuracy of traditional overhead monitoring workflows.