Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study

Some real-world domains, such as Agriculture and Healthcare, comprise early-stage disease indications whose recording constitutes a rare event, and yet, whose precise detection at that stage is critical. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. To simulate such scenarios, we artificially generate skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset as a basis for classification of scarce visual cues through transfer learning. By randomly and unevenly picking healthy and unhealthy samples from certain plant types to form a training set, we consider a base experiment as fine-tuning ResNet34 and VGG19 architectures and then testing the model performance on a balanced dataset of healthy and unhealthy images. We empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19. We demonstrate that utilizing an additional BN layer before the output layer in modern CNN architectures has a considerable impact in terms of minimizing the training time and testing error for minority classes in highly imbalanced data sets. Moreover, when the final BN is employed, minimizing the loss function may not be the best way to assure a high F1 test score for minority classes in such problems. That is, the network might perform better even if it is not confident enough while making a prediction; leading to another discussion about why softmax output is not a good uncertainty measure for DL models.Comment: Accepted for presentation and inclusion in ICPR 2020, the 25th International Conference on Pattern Recognitio

Modal propagation characteristics of radially stratified and D-shaped metallic optical fibres

The eigenvalue equation is formulated for a general three-layered radially stratified metallic optical fiber waveguide and solved numerically using the zoom search method. The result is shown to be applicable to the common D-shaped fiber, which bears no similarity to a concentric stratum but may be converted as such through the Mobius conformal representation. The theoretical prediction agrees well with our experimental measurements, and the method should be proved valuable for optimizing metallic fiber design relationships

In-field hyperspectral imaging dataset of Manzanilla and Gordal olive varieties throughout the season

Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and ‘n’ rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.Hermanos Donaire Ibáñez Agrícola, SC and the Regulatory Council of the PGI Manzanilla and Gordal Olives from Sevill

SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet

Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case disease severity scoring for Cercospora Leaf Spot (CLS) in sugar beet. With concepts of Deep Label Distribution Learning (DLDL), special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems as we show by a pretraining on environmental metadata.Comment: submitted to Computers and Electronics in Agricultur

Modified CycleGAN for the synthesization of samples for wheat head segmentation

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4\% on an internal dataset and Dice scores of 79.6% and 83.6% on two external Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery

UAV Detection of sinapis arvensis infestation in alfalfa plots using simple vegetation indices from conventional digital cameras

Producción CientíficaUnmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps.Unión Europea (project LIFE11 ENV/ES/000535

Resistenzzüchtung / Widerstandsfähigkeit gegen Schadorganismen

115 - Hyperspektrale Charakterisierung von Resistenzreaktionen der Gerste gegenüber Blumeria graminis f.sp. hordeiHyperspectral characterisation of resistance reactions of barley against Blumeria graminis f.sp. hordeiMatheus Kuska, Heinz-Wilhelm Dehne, Ulrike Steiner, Erich-Christian Oerke, Anne-Katrin Mahlein116 - High temperature induced changes in the rice transcriptome under infection with Magnaporthe oryzaeGeoffrey Onaga, Kerstin Wydra, Birger Koopmann, Andreas von Tiedemann117 - Sortenanfälligkeit von Körnermais auf Fusarium-Kolbenfäule in der SchweizSusceptibility to Gibberella ear rot of maize varieties cultivated in SwitzerlandStéphanie Schürch118 - Sensorische Phänotypisierung der Reaktion von Zuckerrübengenotypen auf BlattfleckenerregerSensory phenotyping of the response of sugar beet genotypes to leaf spot pathogensMarlene Leucker, Anne-Katrin Mahlein, Ulrike Steiner, Erich-Christian Oerke120 - Resistenzeigenschaften von Maispflanzen gegen Larven des Westlichen MaiswurzelbohrersResistance properties of maize against Western corn rootworm larvaeMario Schumann, Bianca Tappe, Stefan Vidal121 - Evaluierung der Resistenzeigenschaften von Brassica napus, Sinapis alba sowie Gattungshybriden gegenüber der Kleinen Kohlfliege (Delia radicum L.)Screening of Brassica napus, Sinapis alba and interspecific hybrids for resistance to cabbage root fly (Delia radicum L.)Henrike Hennies, Bernd Ulbe