Investigating Explanatory Factors of Machine Learning Models for Plant Classification

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

Temperature field due to a moving heat source in machining orthotropic composites with arbitrary fiber orientation

Milling of fiber reinforced plastics (FRP) is challenging with respect to surface integrity and tool wear due to high process temperatures. The maximum temperatures occurring in the workpiece determine the extent of the matrix decomposition area. Improving the workpiece quality therefore requires an understanding of its internal temperature distribution during milling. However, steep temperature gradients and high mechanical stress make measurements of temperature fields in the cutting zone difficult. In addition, thermal properties change depending on the fiber orientation in the case of orthotropic FRP. In this paper, an existing model describing the temperature field for isotropic materials is extended to unidirectional, orthotropic FRP. Here, the thermal impact of an end mill upon the machined surface is represented by a strip-shaped heat source which moves with the feed velocity along a semi-infinite space. Starting with the temperature field of an instantaneous point source, multiple integration steps and a coordinate transformation lead to the temperature field of the moving strip source for orthotropic materials with arbitrary fiber orientation. Using this analytical approach, two-dimensional temperature fields within the workpiece can be calculated for various feed velocities, heat source widths and fiber orientation angles. A cross verification of the analytical solution is successfully carried out by comparing it to a numerical simulation. Furthermore, temperature measurements during end milling of carbon fiber reinforced plastics using thermocouples confirm these results for different fiber orientations. The derived model can be applied to a variety of heat flow problems relevant for orthotropic materials, e.g. other machining technologies

Helical milling of bore holes in Ti6Al4V parts produced by selective laser melting with simultaneous support structure removal

Selective Laser Melting (SLM) is a powder bed based Additive Manufacturing (AM) process that is currently being established in the series production of Ti6Al4V components in the aviation industry. One advantage is the significantly lower Buy-to-Fly ratio. However, subsequent machining is necessary in order to remove support structures of the SLM process and to fulfill quality requirements. Experimental results on support structure removal and simultaneous finishing of holes by helical milling are presented. Engagement conditions in helical milling are strongly influenced by the support structure. Material removal rates in both peripheral and axial direction are calculated and agree well with the variation of measured forces in these directions. In addition, the surface roughness of the machined holes is affected by the support structure design and may change along the hole perimeter. The findings indicate how support structures should be designed in order to obtain high quality bore holes in one machining step.The results of this publication have been achieved in the project ALM2AIR funded by the German Federal Ministry for Economic Affairs and Energy under funding code 20W1501M

Comparison of Unsupervised Learning Methods for Natural Image Processing

For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object detection (Ren et al. 2015) or pixel-wise weed classification (Milioto et al. 2017) machine learning is used for both feature extraction and processing (e.g. classification or regression). Historically, feature extraction (e.g. PCA; Ch. 12.1. in Bishop 2006) and processing were sequential and independent tasks (Wöber et al. 2013). Since the rise of convolutional neuronal networks (LeCun et al. 1989), a deep machine learning approach optimized for images, in 2012 (Krizhevsky et al. 2012), feature extraction for image analysis became an automated procedure. A convolutional neuronal net uses a deep architecture of artificial neurons (Goodfellow 2016) for both feature extraction and processing. Based on prior information such as image classes and supervised learning procedures, parameters of the neuronal nets are adjusted. This is known as the learning process. Simultaneously, geometric morphometrics (Tibihika et al. 2018, Cadrin and Friedland 1999) are used in biodiversity research for association analysis. Those approaches use deterministic two-dimensional locations on digital images (landmarks; Mitteroecker et al. 2013), where each position corresponds to biologically relevant regions of interest. Since this methodology is based on scientific results and compresses image content into deterministic landmarks, no uncertainty regarding those landmark positions is taken into account, which leads to information loss (Pearl 1988). Both, the reduction of this loss and novel knowledge detection, can be done using machine learning. Supervised learning methods (e.g., neuronal nets or support vector machines (Ch. 5 and 6. in Bishop 2006)) map data on prior information (e.g. labels). This increases the performance of classification or regression but affects the latent representation of the data itself. Unsupervised learning (e.g. latent variable models) uses assumptions concerning data structures to extract latent representations without prior information. Those representations does not have to be useful for data processing such as classification and due to that, the use of supervised and unsupervised machine learning and combinations of both, needs to be chosen carefully, according to the application and data. In this work, we discuss unsupervised learning algorithms in terms of explainability, performance and theoretical restrictions in context of known deep learning restrictions (Marcus 2018, Szegedy et al. 2014, Su et al. 2017). We analyse extracted features based on multiple image datasets and discuss shortcomings and performance for processing (e.g. reconstruction error or complexity measurement (Pincus 1997)) using the principal component analysis (Wöber et al. 2013), independent component analysis (Stone 2004), deep neuronal nets (auto encoders; Ch. 14 in Goodfellow 2016) and Gaussian process latent variable models (Titsias and Lawrence 2010, Lawrence 2005)