Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops

Abstract

Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. We propose a point supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets - Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time.

Plant phenotyping relevance

穀粒穂の密度を対象とする画像ベースの検出・アクティブラーニング手法を開発し、複数作物データセットで性能とラベリングコストを評価しており、表現型取得手法が中心である。

Code and data availability

The supplied blocks describe experiments on two publicly available cereal crop datasets (Wheat from Madec et al., Sorghum from Guo et al.), but contain no authors' code release, data deposit, or availability statement with a public URL for this paper's own assets. The only URLs present are the article DOI and Creative-

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