Franchetti Benjamin, Pirri Fiora
Agricola Moderna, Milan, Italy.
Alcor Lab, DIAG, Sapienza University of Rome, Rome, Italy.
Front Plant Sci. 2022 May 12;13:874035. doi: 10.3389/fpls.2022.874035. eCollection 2022.
Recent years have seen an increased effort in the detection of plant stresses and diseases using non-invasive sensors and deep learning methods. Nonetheless, no studies have been made on dense plant canopies, due to the difficulty in automatically zooming into each plant, especially in outdoor conditions. Zooming in and zooming out is necessary to focus on the plant stress and to precisely localize the stress within the canopy, for further analysis and intervention. This work concentrates on tip-burn, which is a plant stress affecting lettuce grown in controlled environmental conditions, such as in plant factories. We present a new method for tip-burn stress detection and localization, combining both classification and self-supervised segmentation to detect, localize, and closely segment the stressed regions. Starting with images of a dense canopy collecting about 1,000 plants, the proposed method is able to zoom into the tip-burn region of a single plant, covering less than 1/10th of the plant itself. The method is crucial for solving the manual phenotyping that is required in plant factories. The precise localization of the stress within the plant, of the plant within the tray, and of the tray within the table canopy allows to automatically deliver statistics and causal annotations. We have tested our method on different data sets, which do not provide any ground truth segmentation mask, neither for the leaves nor for the stresses; therefore, the results on the self-supervised segmentation is even more impressive. Results show that the accuracy for both classification and self supervised segmentation is new and efficacious. Finally, the data set used for training test and validation is currently available on demand.
近年来，人们越来越努力地利用非侵入式传感器和深度学习方法来检测植物胁迫和疾病。然而，由于难以自动聚焦到每一株植物上，特别是在户外条件下，尚未对密集的植物冠层进行研究。放大和缩小对于关注植物胁迫以及精确确定冠层内胁迫的位置以便进行进一步分析和干预是必要的。这项工作专注于尖端灼伤，这是一种影响在可控环境条件下（如植物工厂）种植的生菜的植物胁迫。我们提出了一种用于尖端灼伤胁迫检测和定位的新方法，该方法结合了分类和自监督分割，以检测、定位并紧密分割受胁迫区域。从收集约1000株植物的密集冠层图像开始，所提出的方法能够放大到单株植物的尖端灼伤区域，该区域覆盖的植物自身面积不到十分之一。该方法对于解决植物工厂中所需的人工表型分析至关重要。植物内胁迫、托盘内植物以及冠层内托盘的精确定位允许自动提供统计数据和因果注释。我们在不同的数据集上测试了我们的方法，这些数据集既没有提供叶子的也没有提供胁迫的任何地面真值分割掩码；因此，自监督分割的结果更令人印象深刻。结果表明，分类和自监督分割的准确率都是新颖且有效的。最后，用于训练、测试和验证的数据集目前可按需获取。
