TY  - CHAP
A1  - Schwaiger, Simon
A1  - Aburaia, Mohamed
A1  - Aburaia, Ali
A1  - Wöber, Wilfried
T1  - Explainable Artificial Intelligence for Robot Arm Control
T2  - Proceedings of the 32nd International DAAAM Virtual Symposium `Intelligent Manufacturing & Automation`, 28-29th October 2021, Vienna
KW  - Artificial Intelligence
KW  - Machine Learning
Y1  - 
VL  - 32
IS  - 1
SP  - 0640
EP  - 0647
ER  - 
TY  - CHAP
A1  - Abdank, Moritz
A1  - Aburaia, Mohamed
A1  - Wöber, Wilfried
T1  - Using-Colour-Based Object Detection for Pick and Place Applications
T2  - Proceedings of the 32nd International DAAAM Virtual Symposium 'Intelligent Manufacturing & Automation', 28-29th October 2021, Vienna
KW  - Computer Vision
KW  - Object Detection
KW  - ROS
Y1  - 
VL  - 32
IS  - 1
SP  - 0536
EP  - 0541
ER  - 
TY  - JOUR
A1  - Wöber, Wilfried
A1  - Curto, Manuel
A1  - Tibihika, Papius D.
A1  - Meulenboek, Paul
A1  - Alemayehu, Esayas
A1  - Mehnen, Lars
A1  - Meimberg, Harald
A1  - Sykacek, Peter
T1  - Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning
JF  - PlosONE
KW  - Machine Learning
Y1  - 
VL  - 16
IS  - 4
ER  - 
TY  - JOUR
A1  - Wöber, Wilfried
A1  - Mehnen, Lars
A1  - Sykacek, Peter
A1  - Meimberg, Harald
T1  - Investigating Explanatory Factors of Machine Learning Models for Plant Classification
JF  - Plants
N2  - Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24%
and 96.10% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08% and 94.31% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38% and 93.28%
mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications. View Full-Text
Keywords: deep learning; machine learning; plant leaf morphometrics; explainable AI
KW  - deep learning
KW  - machine learning
KW  - plant leaf morphometrics
KW  - explainable AI
Y1  - 
VL  - 2021
IS  - 10(12):2674
ER  -