MT-Det: A novel fast object detector of maize tassel from high-resolution imagery using single level feature

Abstract

Accurately and quickly detecting maize tassels is crucial for successful maize breeding and seed production. However, the complex planting environment in the field, including unsynchronized growth stages, cluttered backgrounds, severe occlusions, and varying tassel sizes and shapes, presents a challenge for detection methods. Current methods either use mainstream object detectors off-the-shelf or employ shallow adaptations on the modular level, lacking a systematic design that balances both speed and accuracy. Furthermore, most studies only report evaluation results on sliced image patches instead of original high-resolution images, which can offer limited guidance for large-scale crop management. In this study, we propose a novel one-stage anchor-free maize tassel detector (MT-Det), which is based on a single level feature and designed to be simple yet effective. Extensive comparisons with both one-level counterparts and feature pyramid detectors demonstrate that MT-Det outperforms them in both detection accuracy and inference speed. To address the issue of significant accuracy drop when directly inferring on high-resolution images, we introduce a bundle of slicing-aided hyper inference technologies to our proposed MT-Det, which results in a 13% and 38% improvement of mean average precision (mAP) on proximal and unmanned aerial vehicle (UAV) high-resolution images, respectively. We believe that MT-Det provides a promising high-throughput solution for accurate and efficient detection and counting of maize tassels in real-world field conditions.

Plant phenotyping relevance

トウモロコシ雄穂の検出・計数という植物器官形質の取得を目的に、画像検出器と高解像度画像向け推論技術を開発・比較評価しており、表現型取得手法が中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.