Fig 1.
A proposed methodology for the development of computer-aided identification of earthworm species (E. Fetida) using machine learning and digital images.
Table 1.
Predictive performance for earthworm species prediction across different classification models and handcrafted features using 10-fold CV (E. fetida vs others).
Table 2.
Predictive performance for earthworm species prediction across different classification models and handcrafted features on external validation dataset (E. fetida vs others).
Fig 2.
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves showing predictive performance of our proposed model for the classification of digital images of earthworms across different classifiers (SVM, RF, XGB) and DenseNet feature map on an external validation dataset.
E. fetida vs others: ROC(A), PR(B).
Table 3.
Predictive performance for earthworm species prediction across different classification models and deep feature maps using 10-fold CV (E. fetida vs others).
Table 4.
Predictive performance for earthworm species prediction across different classification models and deep feature maps on external validation dataset (E. fetida vs others).
Fig 3.
Some of the images of earthworm species (E. fetida and other) used to test ESIDE in a real use under the supervision of a qualified taxonomist.
Fig 4.
Confusion matrices: Showing the performance of our proposed model for earthworm species identification in a real setting under the supervision of a qualified taxonomist.