High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model.

Abstract

Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second.

Plant phenotyping relevance

コムギ穂数という植物器官形質を画像から検出・計数するモデルを開発し、性能比較とモバイルアプリ化まで行っており、表現型取得手法が研究の中心である。

Code and data availability

The paper's wheat head detection model was trained and evaluated on the public Global Wheat Head Detection dataset hosted on Kaggle, which directly provides the plant-phenotyping images and bounding-box annotations used in this study. No authors' code or trained model repository is disclosed; the Data Availability only