The Effects of Crop Type and Production Systems on the Activity of Beneficial Invertebrates

Beneficial invertebrate activity (13 groups) was assessed in five crop types on a split-plot experimental system in northern England using pitfall trapping and suction sampling in May-October 2005. Very significant differences were detected in activity between crop type, and in the preference of groups for individual crops. Within crop types, differences in fertiliser and crop protection approaches appeared to significantly affect activity, with preferences for either organic or conventional management differing between groups. In general, inorganic fertiliser application had more effect on activity than pesticide, herbicide and fungicide use

Effect of organic, low-input and conventional production systems on yield and diseases in winter barley

The effect of organic, low-input and conventional management practices on barley yield and disease incidence was assessed in field trials over two years. Conventional fertility management (based on mineral fertiliser applications) and conventional crop protection (based on chemosynthetic pesticides) significantly increased the yield of winter barley as compared to organic fertility and crop protection regimes. Severity of leaf blotch (Rhynchosporium secalis) was highest under organic fertility and crop protection management and was correlated inversely with yield. For mildew (Erysiphe graminis), an interaction between fertility management and crop protection was detected. Conventional crop protection reduced severity of the disease, only under conventional fertility management. Under organic fertility management, incidence of mildew was low and application of synthetic pesticides in “low input” production systems had no significant effect on disease severity

N released from organic amendments is affected by soil management history

A ryegrass bioassay was conducted to investigate the effect of soil management history on nitrogen mineralisation from composted manure and pelleted poultry manure. Soils were used from 2 field experiments comparing conventional and organic/low input management systems. When composted manure was added, soils which had received high rates of composted FYM under biodynamic management released a greater amount of nitrogen for plant uptake than those with a history of mineral or fresh manure fertilisation, suggesting that biological preconditioning may result in greater efficiency of composted FYM as a nitrogen source for plants. “Native” N mineralisation was found to be related to total soil N content

Effect of organic, low-input and conventional production systems on pesticide and growth regulator residues in wheat, potato and cabbage

The Nafferton factorial systems comparison (NFSC) experiments facilitate the investigation of effects of, and interaction between, three production system components - a) rotational position, b) fertility and c) crop protection management - in organic, conventional and low-input crop management systems. This paper presents first results on pesticide and growth regulator residues observed over a period of two years. Residues were only detected for three (Chlormequat, Chlorothalonil and Aldicarb) of the 28 pesticides used in the experiments. As expected, residue levels were affected by the crop protection practices, but significant effects of fertility management practices were also detected. This indicates that the human health risks associated with pesticide residues may increase in low input systems which attempt to reduce the environmental impact of conventional farming systems by switching to organic matter based fertilisation regimes

ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

Social sensing of floods in the UK

This is the final version of the article. Available from Public Library of Science (PLoS) via the DOI in this record.“Social sensing” is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes ‘relevance’ filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter

Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201

Outlier detection of vital sign trajectories from COVID-19 patients

There is growing interest in continuous wearable vital sign sensors for monitoring patients remotely at home. These monitors are usually coupled to an alerting system, which is triggered when vital sign measurements fall outside a predefined normal range. Trends in vital signs, such as an increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems. In this work, we present a novel outlier detection algorithm to identify such abnormal vital sign trends. We introduce a distance-based measure to compare vital sign trajectories. For each patient in our dataset, we split vital sign time series into 180 minute, non-overlapping epochs. We then calculated a distance between all pairs of epochs using the dynamic time warp distance. Each epoch was characterized by its mean pairwise distance (average link distance) to all other epochs, with large distances considered as outliers. We applied this method to a pilot dataset collected over 1561 patient-hours from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show that outlier epochs correspond well with patients who were subsequently readmitted to hospital. We also show, descriptively, how epochs transition from normal to abnormal for one such patient.Comment: 4 pages, 4 figures, 1 table. Submitted to IEEE BHI 2022, decision pendin

Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks

This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multi-person, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.Comment: Submitted to the 2018 IEEE International Conference on Robotics and Automatio