Peach Flower Monitoring Using Aerial Multispectral Imaging

Abstract

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

Plant phenotyping relevance

モモ花の検出という植物器官の状態を、UASマルチスペクトル画像と植生指数に基づく画像処理アルゴリズムで取得する方法を開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The paper describes UAS multispectral image acquisition and MATLAB-based blossom detection, but contains no data or code availability statement, no public repository deposit, and no author-provided URL for images, datasets, or scripts. The only URLs present (USDA NASS, DJI, DroneDeploy, CC-BY license) are cited tools,

No evidence-backed public reproduction asset is currently recorded.