Erratum to "Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning".

[This corrects the article DOI: 10.34133/plantphenomics.0022.].

Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning.

Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.

Active W Sites Promoted by Defect Engineering Enhanced C2H6S3 Sensing Performance of WO3 Nanosheets.

Defect engineering has received extensive attention as an effective method to tune the gas sensing properties of semiconductor materials. Here, defective WO3 (D-WO3) nanosheets were obtained by a simple hydrogenation process with a detection limit as low as 5 ppb for dimethyl trisulfide (DMTS) and a response of 2.3 times that of the initial WO3 nanosheets to 100 ppb DMTS. Importantly, X-ray photoelectron spectroscopy and Raman spectroscopy confirmed the partial loss of oxygen atoms in D-WO3 nanosheets, and density functional theory calculations found that the W sites near the oxygen defect showed higher adsorption energy for DMTS and transferred more electrons during the gas interaction, indicating that the active W site caused by oxygen atom loss can effectively enhance the reactivity of two-dimensional WO3 nanosheets. Different from the traditional oxygen defect model, this work reveals the positive effect of active metal sites on gas sensing for the first time, which is expected to provide an effective reference for the sensing application of defect engineering in metal oxides.