Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest
- Do herbaria contain relevant information for the study of tropical tree growth? If so, to what extent?
- Can deep learning-based automated approaches detect growing specimens?
- If so, which approaches are most relevant and which growth patterns are best detected?
2.1. Herbarium Data
2.2. Global Model Detection Performance (EXP1, ResNet50)
2.3. Local Models Detection Performance (EXP2, Faster R-CNN, and Mask R-CNN)
2.4. Local Models Two-Class Detection Performance (EXP3, Faster R-CNN, and Mask R-CNN)
- Collect more specimens with growing shoots in addition to those with reproductive structures;
- Organize the specimens on the herbarium sheet in order to better visualize the ends of the axes, and to avoid leaf overlaps;
- Annotate the presence or absence of new growing shoots.
4. Materials and Methods
4.1. Assembling Herbarium Records and Manual Annotations
- “FullImage” level. All herbarium sheets used in this study were associated with a tag (Yes or No) indicating if the sheet had at least one recent growing shoot visible or not. No information on the location, number, or size of recent growing shoots was recorded.
- “Mask” level. In each herbarium sheet containing one or several recent growing shoots, each shoot was manually annotated and cross-validated by two co-authors using COCO Annotator (https://github.com/jsbroks/coco-annotator accessed on 10 May 2021) a web-based image annotation tool designed to efficiently label images and create training data for object detection tasks (see Figure 6). This allows for the precise capture of their full shape. It should be noted that it was not uncommon for growing shoots to be partially overlapped by other plant parts (leaves of adult sizes, branches, petioles) or sometimes by pieces of tape. In this case, only the visible part in the foreground was annotated, thus generating a mask potentially based on several disjointed polygons for a single growing shoot.
- “BoundingBox” level. Once the masks were created, the bounding boxes were directly deduced from the masks based on the min and max coordinates in x and y, respectively, for each mask. This gives us rectangles that capture both the growing shoots and the surrounding visual context such as parts of stems, leaves, textual description, and paper.
4.2. Preliminary Analysis of the Annotations
4.3. Description of Experiments
- Detection: What is the performance of automatic growing shoot detection in herbaria? Do we obtain performances comparable to the automatic detection of reproductive structures? With what proportions of missed growing shoots and detection errors?
- Detection and classification: Is it possible to both detect and classify different types of growing patterns (“Continuous” or “Rhythmic”) automatically?
4.4. Evaluated Deep Learning Architectures
- Global model (ResNet50). The first deep learning model that was trained is the Convolutional Neural Network (CNN) ResNet50 . It was pretrained on the ImageNet dataset  and fine-tuned on our training dataset. ResNet50 is widely used in image classification tasks and research works for its good compromise between performance, memory use, and training time. Moreover, ResNet50 is often preferred to other recent architectures for a wide range of application studies because its architecture is rather simple, and it is relatively easy to find training hyperparameters that produce good and stable results. A CNN produces as outputs a list of classification scores (probabilities) related to the considered categories, but without any information about the sub-parts of the image that contributed to the prediction. Pre-trained models can be easily found for most Deep Learning frameworks, particularly for PyTorch (https://pytorch.org/, accessed on 10 May 2021), which was used for our experiments. Details on this model adaptation, data augmentation strategy, and the used hyperparameters are provided in Appendix A.
- Local model (Faster R-CNN). The second model that we evaluated is based on the Faster R-CNN architecture , which was chosen for its demonstrated efficiency in various object detection tasks and challenges such as MS COCO . A trained Faster R-CNN model produces as outputs a list of bounding boxes associated with probabilities related to the considered categories for detection. We used the Detectron2 implementation  itself using the PyTorch framework, based on ResNet50 as the backbone CNN and the Feature Pyramid Network  as the Region Proposal Network for object detection. The total number of training iterations was made based on the empirical observation of the model’s training performance. A detailed description of the hyperparameters that were used to train the model is provided in Appendix B.
- Local model (Mask R-CNN). The third model that we evaluated is based on the Mask R-CNN architecture , which was chosen for its ability to perform an instance segmentation task by extending the Faster R-CNN approach to a pixel-level mask prediction task. A trained Mask R-CNN model produces as outputs a list of polygon sets, each associated with probabilities related to the considered categories. As for the Faster R-CNN, we used the Detectron2 implementation , using by default the same backbone ResNet50 and the Feature Pyramid Networks as for Faster R-CNN. A detailed description of the hyperparameters that were used to train the model is provided in Appendix B.
4.5. Assessing Raw Performances of Deep Learning Models
4.5.1. Training and Test Datasets
- EXP1: Detection, Global model (ResNet50)
- EXP2: Detection, Local models (Faster R-CNN, Mask R-CNN)
- EXP3: Detection and Classification, Local models (Faster R-CNN, Mask R-CNN)
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|TPR||True Positive Rate|
|FPR||False Positive Rate|
|ROC||Receiver Operating Characteristic|
|ROC AUC||Area Under the Receiver Operating Characteristic Curve|
|CNN||Convolutional Neural Network|
|ResNet||Residual Convolutional Neural Network|
|Faster R-CNN||Faster Region-Based Convolutional Neural Network|
|Mask R-CNN||Mask Region-Based Convolutional Neural Network|
Appendix A. Global Model (ResNet50)
Appendix B. Local Models (Faster R-CNN and Mask R-CNN)
- The images were resized with a minimum size of 1000 pixels;
- The anchor size values were set to [32; 64; 128; 256; 512] with aspect ratios of [0.5; 1; 2];
- An NMS threshold set to 0.7;
- An initial learning rate of 0.002, with a rate decay of 6:2:2 (the initial learning rate value was divided by 10 at six-tenths and eight-tenths of the training);
- Random horizontal flips and random rotations of ±45 degrees as data augmentation;
- Batch size of three images per iteration;
- A maximum of 10,000 iterations (approximately 74 epochs), with snaphots every 1 k iterations to select the best model (see below).
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|Faster R-CNN (local)||0.44||0.80|
|Mask R-CNN (local)||0.48||0.60||0.68|
|Growing Shoot Type||Model||0.10||0.20||0.30|
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Goëau, H.; Lorieul, T.; Heuret, P.; Joly, A.; Bonnet, P. Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest. Plants 2022, 11, 530. https://doi.org/10.3390/plants11040530
Goëau H, Lorieul T, Heuret P, Joly A, Bonnet P. Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest. Plants. 2022; 11(4):530. https://doi.org/10.3390/plants11040530Chicago/Turabian Style
Goëau, Hervé, Titouan Lorieul, Patrick Heuret, Alexis Joly, and Pierre Bonnet. 2022. "Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest" Plants 11, no. 4: 530. https://doi.org/10.3390/plants11040530