In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds machinery. Detection recognition wildlife within fields is important reduce mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes automated detection classification thermal imaging. methods results based on top-view images taken manually from a lift motivate towards unmanned aerial vehicle-based recognition....

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms trained classifying a predefined set of types. These have difficulties distant heavily occluded objects are, by definition, not capable unknown types or unusual scenarios. The visual characteristics an agriculture field is homogeneous, obstacles, like people, animals other occur rarely...

Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading therefore commonly used to select the embryo with highest potential. One challenges, however, that can be highly subjective when performed manually by embryologists. Grading systems generally discretize continuous scale low high score, resulting floating and unclear boundaries between categories. Manual annotations suffer from large inter-and intra-observer...

Abstract Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities fertilized oocytes (embryos) and selecting best embryo(s) available a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve automate embryo ranking procedure by extracting relevant information from microscopy images. The AI models are evaluated based on their ability identify with highest chance(s)...

Cameras and computer vision are revolutionising the study of insects, creating new research opportunities within agriculture, epidemiology, evolution, ecology monitoring biodiversity. However, diversity insects close resemblances many species a major challenge for image-based species-level classification. Here, we present an algorithm to hierarchically classify from images, leveraging simple taxonomy (1) specimens across multiple taxonomic ranks simultaneously, (2) identify lowest rank at...

It is hard to create consistent ground truth data for interest points in natural images, since are define clearly and consistently a human annotator. This makes point detectors non-trivial build. In this work, we introduce an unsupervised deep learning-based detector descriptor. Using self-supervised approach, utilize siamese network novel loss function that enables scores positions be learned automatically. The resulting descriptor UnsuperPoint. We use regression of 1) make UnsuperPoint...

GrassClover is a diverse image and biomass dataset collected in an outdoor agricultural setting. The images contain dense populations of grass clover mixtures with heavy occlusions occurrences weeds. Fertilization treatment mixed crops depend on the local species composition. Therefore, overall challenge related to predicting composition canopy biomass. three different acquisition systems ground sampling distances 4-8 px/mm. observed vary both setting (field vs plot trial), seed...

The presence of clouds is widely identified as the primary uncertainty in current surface solar global horizontal irradiance (GHI) forecasts. Despite a wealth historical satellite-derived observations, only limited research has investigated this problem from purely data-driven perspective, something that seen tremendous success related domains such radar- and satellite-based precipitation short-term forecasting. This paper presents IrradianceNet, novel neural network for spatiotemporal...

As pollinators, insects play a crucial role in ecosystem management and world food production. However, insect populations are declining, necessitating efficient monitoring methods. Existing methods analyze video or time-lapse images of nature, but analysis is challenging as small objects complex dynamic natural vegetation scenes. In this work, we provide dataset primarily honeybees visiting three different plant species during two months the summer. The consists 107,387 annotated from...

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The comprises approximately 2 h of raw sensor data from tractor-mounted system grass mowing scenario Denmark, October 2016. Sensing modalities include stereo camera, thermal web 360 ∘ LiDAR and radar, while precise localization is available fused IMU GNSS. Both static moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles vegetation. All have ground truth...

Optimal fertilization of clover-grass fields relies on knowledge the clover and grass fractions. This study shows how can be obtained by analyzing images collected in automatically. A fully convolutional neural network was trained to create a pixel-wise classification clover, grass, weeds red, green, blue (RGB) mixtures. The estimated fractions dry matter from were found highly correlated with real matter, making this cheap non-destructive way monitoring fields. solely simulated top-down...

Abstract Insects represent nearly half of all known multicellular species, but knowledge about them lags behind for most vertebrate species. In part this reason, they are often neglected in biodiversity conservation policies and practice. Computer vision tools, such as insect camera traps, automated monitoring have the potential to revolutionize study conservation. To further advance trapping analysis their image data, effective processing pipelines needed. paper, we present a flexible fast...

Abstract Arthropods, including insects, represent the most diverse group and contribute significantly to animal biomass. Automatic monitoring of insects other arthropods enables quick efficient observation management ecologically economically important targets such as pollinators, natural enemies, disease vectors, agricultural pests. The integration cameras computer vision facilitates innovative approaches for agriculture, ecology, entomology, evolution, biodiversity. However, studying their...

The increasing number of people choosing to travel by airplane puts pressure on the baggage handling systems in airports. As load increases, risk deadlocks increase as well. Therefore, it is increasingly important find routing solutions which can handle high loads. Currently this achieved using shortest path algorithms and hand engineered site-specific rules, based experience employees trial error processes complex emulators. This a time-consuming costly approach, every airport needs its own...

With self-supervised learning, both labeled and unlabeled data can be used for representation learning model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during fertility treatment, in which only embryos that were transferred to female uterus may have labels pregnancy. In this paper, we apply video alignment method known as temporal cycle-consistency (TCC) on 38176 time-lapse videos developing embryos, 14550 labeled. We...

In this paper, we introduce a novel approach to estimate the illumination and reflectance of an image. The is based on illumination-reflectance model wavelet theory. We use homomorphic filter (HWF) define quotient image (WQI) dyadic transform. components are estimated by using HWF WQI, respectively. Based estimation develop algorithm segment sows in grayscale video recordings which captured complex farrowing pens. Experimental results demonstrate that can be applied detect domestic animals...

This paper develops a simple and effective method for detection of sows piglets in grayscale video recordings farrowing pens. approach consists three stages: background updating, calculation pseudo-wavelet coefficients foreground object segmentation. In the first stage, texture integration is used to update modelling (i.e. reference image). second we apply an "à trous" wavelet transform on current image then perform subtraction between original approximation image. third pairwise...

Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has potential increase value, it comes at a higher expense terms of field surveys. As fine-grained distribution mapping within-field variation is typically unfeasible, management remains an open research area. In this work, we propose new method for determining biomass composition from high resolution color images...