An End-to-End Precision Phenotyping Framework: Rice Panicle Detection and Counting in Complex Fields via Lightweight DETR

Accurate, high-throughput quantification of rice panicles plays a vital role in advancing precision yield prediction. However, transitioning to real-time, edge-deployable unmanned aerial vehicle phenotyping is often impeded by extreme spatial scale variations from altitude fluctuations and complex unstructured backgrounds. To address this, we constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions. We then propose Panicle-DETR, a highly optimized precision phenotyping framework incorporating a frequency-aware CSP backbone. By projecting visual perception into the frequency domain, the architecture inherently suppresses low-frequency environmental noise and minimizes computational redundancy. Furthermore, a Lossless Feature Encoder prevents the irreversible pixel decimation of micro-targets across varying operational altitudes, while a composite metric loss explicitly disentangles heavily adhered panicle clusters. Evaluated on our composite dataset, Panicle-DETR achieved an outstanding detection Precision of 90.97% alongside robust agronomic counting stability, demonstrated by a Mean Absolute Error of 4.28 and an \( R^2 \) of 0.957. With a compact footprint of only 13.78 M parameters, this framework fundamentally overcomes the computational and spatial limitations of traditional vision models, establishing a highly reliable paradigm for autonomous, onboard agricultural monitoring.