Detection and Localization of Tip-Burn on Large Lettuce Canopies - Franchetti, Benjamin; Pirri, Fiora (Frontiers in Plant Science, 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 onto 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 environment 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 one thousand plants the proposed method is able to zoom into the tip-burn region of a single plant, covering less than 1/10 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 both for classification and self-supervised segmentation is new and efficacious. Finally, the data set used for training test and validation is currently available on demand. We have tested our method on different datasets, which did not require segmentation 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 both for classification and self supervised segmentation is new and efficacious. Finally, the dataset used for training test and validation is currently available on demand.