Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves

Abstract

Precision agriculture leverages data and machine learning so that farmers can monitor their crops and target interventions precisely. This enables the precision application of herbicide only to weeds, or the precision application of fertilizer only to undernourished crops, rather than to the entire field. The approach promises to maximize yields while minimizing resource use and harm to the surrounding environment. To this end, we propose a hierarchical panoptic segmentation method that simultaneously determines leaf count (as an identifier of plant growth)and locates weeds within an image. In particular, our approach aims to improve the segmentation of smaller instances like the leaves and weeds by incorporating focal loss and boundary loss. Not only does this result in competitive performance, achieving a PQ+ of 81.89 on the standard training set, but we also demonstrate we can improve leaf-counting accuracy with our method. The code is available at https://github.com/madeleinedarbyshire/HierarchicalMask2Former.

Plant phenotyping relevance

植物・葉の階層的パノプティックセグメンテーション法を開発し、葉数という植物成長形質の推定精度を評価しているため、フェノタイピング手法が中心である。

Code and data availability

The paper's own analysis code is publicly declared (github.com/madeleinedarbyshire/HierarchicalMask2Former), but that URL is not among the allowed_urls, so it cannot be listed. The PhenoBench dataset is cited prior work, and the CVPPA 2023 challenge site is a competition page, not a paper-specific asset. No qualifying,

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