Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction.

Abstract

Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important. We investigate which data acquisition days and development stages positively affect the model accuracy to get insights into prediction-relevant observation days and aid future data acquisition planning. We analyze harvest-readiness using the cauliflower image time series of the GrowliFlower dataset. We use an adjusted ResNet18 classification model, including positional encoding of the data acquisition dates to add implicit information about development. The explainable machine learning approach GroupSHAP analyzes time points' contributions. Time points with the lowest mean absolute contribution are excluded from the time series to determine their effect on model accuracy. Using image time series rather than single time points, we achieve an increase in accuracy of 4%. GroupSHAP allows the selection of time points that positively affect the model accuracy. By using seven selected time points instead of all 11 ones, the accuracy improves by an additional 4%, resulting in an overall accuracy of 89.3%. The selection of time points may therefore lead to a reduction in data collection in the future.

Plant phenotyping relevance

カリフラワー画像時系列を用いた収穫適期という植物状態の推定手法を開発・評価し、データ取得時点の選択とモデル精度を検証しているため、フェノタイピング手法が中心である。

Code and data availability

The paper analyzes the GrowliFlower cauliflower UAV image time series dataset and provides a public data availability link to the dataset metadata on phenoroam.phenorob.de. No author analysis code or trained model checkpoint is explicitly deposited.