Lettuce Growth Pattern Analysis Using U-Net Pre-Trained with Arabidopsis

Abstract

To overcome the challenges related to food security, digital farming has been proposed, wherein the status of a plant using various sensors could be determined in real time. The high-throughput phenotyping platform (HTPP) and analysis with deep learning (DL) are increasingly being used but require a lot of resources. For botanists who have no prior knowledge of DL, the image analysis method is relatively easy to use. Hence, we aimed to explore a pre-trained Arabidopsis DL model to extract the projected area (PA) for lettuce growth pattern analysis. The accuracies of the extract PA of the lettuce cultivar “Nul-chung” with a pre-trained model was measured using the Jaccard Index, and the median value was 0.88 and 0.87 in two environments. Moreover, the growth pattern of green lettuce showed reproducible results in the same environment (p < 0.05). The pre-trained model successfully extracted the time-series PA of lettuce under two lighting conditions (p < 0.05), showing the potential application of a pre-trained DL model of target species in the study of traits in non-target species under various environmental conditions. Botanists and farmers would benefit from fewer challenges when applying up-to-date DL in crop analysis when few resources are available for image analysis of a target crop.

Plant phenotyping relevance

植物画像から投影面積を抽出する事前学習U-Netモデルを適用・精度検証し、レタスの時系列成長形質を推定する方法が研究の中心である。

Code and data availability

The paper's lettuce projected-area (PA) time-series phenotype measurements and image analysis pipeline are not publicly deposited. The Data Availability Statement explicitly restricts access: 'Data available upon request.' The online supplement (Figure S1, Table S2) only presents summarized growth-pattern results, not

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