Effect of training systems and pruning methods on fruit quality in apple

The introduction of dwarfed rootstocks in apple crop has led to a new concept of intensive planting systems with the aim of producing early high yield and with returns of the initial high investment. Although yield is an important aspect to the grower, the consumer has become demanding regards fruit quality and is generally attracted by appearance. To fulfil the consumer’s expectations the grower may need to choose a proper training system along with an ideal pruning technique, which ensure a good light distribution in different parts of the canopy and a marketable fruit quality in terms of size and skin colour. Although these aspects are important, these fruits might not reach the proper ripening stage within the canopy because they are often heterogeneous. To describe the variability present in a tree, a software (PlantToon®), was used to recreate the tree architecture in 3D in the two training systems. The ripening stage of each of the fruits was determined using a non-destructive device (DA-Meter), thus allowing to estimate the fruit ripening variability. This study deals with some of the main parameters that can influence fruit quality and ripening stage within the canopy and orchard management techniques that can ameliorate a ripening fruit homogeneity. Significant differences in fruit quality were found within the canopies due to their position, flowering time and bud wood age. Bi-axis appeared to be suitable for high density planting, even though the fruit quality traits resulted often similar to those obtained with a Slender Spindle, suggesting similar fruit light availability within the canopies. Crop load confirmed to be an important factor that influenced fruit quality as much as the interesting innovative pruning method “Click”, in intensive planting systems

Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data

The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles. One of the first steps during the processing of radar point clouds is often the detection of clutter, i.e. erroneous points that do not correspond to real objects. Another common objective is the semantic segmentation of moving road users. These two problems are handled strictly separate from each other in literature. The employed neural networks are always focused entirely on only one of the tasks. In contrast to this, we examine ways to solve both tasks at the same time with a single jointly used model. In addition to a new augmented multi-head architecture, we also devise a method to represent a network's predictions for the two tasks with only one output value. This novel approach allows us to solve the tasks simultaneously with the same inference time as a conventional task-specific model. In an extensive evaluation, we show that our setup is highly effective and outperforms every existing network for semantic segmentation on the RadarScenes dataset.Comment: Published at IEEE International Conference on Intelligent Transportation Systems (ITSC), Bilbao, ESP, 202

Energy-based Detection of Adverse Weather Effects in LiDAR Data

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We reformulate this problem as an outlier detection task and use an energy-based framework to detect outliers in point clouds. More specifically, our method learns to associate low energy scores with inlier points and high energy scores with outliers allowing for robust detection of adverse weather effects. In extensive experiments, we show that our method performs better in adverse weather detection and has higher robustness to unseen weather effects than previous state-of-the-art methods. Furthermore, we show how our method can be used to perform simultaneous outlier detection and semantic segmentation. Finally, to help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset, which contains labeled vehicle spray data in highway-like scenarios. The dataset is available at http://dx.doi.org/10.18725/OPARU-48815 .Comment: Accepted for publication in IEEE Robotics and Automation Letters (RA-L

Robust 3D Object Detection in Cold Weather Conditions

Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditions. This everyday effect can influence the estimation of object sizes, orientations and introduce ghost object detections, compromising the reliability of the state of the art object detectors. We propose to solve this problem by using data augmentation and a novel training loss term. To effectively train deep neural networks, a large set of labeled data is needed. In case of adverse weather conditions, this process can be extremely laborious and expensive. We address this issue in two steps: First, we present a gas exhaust data generation method based on 3D surface reconstruction and sampling which allows us to generate large sets of gas exhaust clouds from a small pool of labeled data. Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions. Finally, we formulate a new training loss term that leverages the augmented point cloud to increase object detection robustness by penalizing predictions that include noise. In contrast to other works, our method can be used with both grid-based and point-based detectors. Moreover, since our approach does not require any network architecture changes, inference times remain unchanged. Experimental results on real data show that our proposed method greatly increases robustness to gas exhaust and noisy data.Comment: Ora

Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds

LiDAR sensors used in autonomous driving applications are negatively affected by adverse weather conditions. One common, but understudied effect, is the condensation of vehicle gas exhaust in cold weather. This everyday phenomenon can severely impact the quality of LiDAR measurements, resulting in a less accurate environment perception by creating artifacts like ghost object detections. In the literature, the semantic segmentation of adverse weather effects like rain and fog is achieved using learning-based approaches. However, such methods require large sets of labeled data, which can be extremely expensive and laborious to get. We address this problem by presenting a two-step approach for the detection of condensed vehicle gas exhaust. First, we identify for each vehicle in a scene its emission area and detect gas exhaust if present. Then, isolated clouds are detected by modeling through time the regions of space where gas exhaust is likely to be present. We test our method on real urban data, showing that our approach can reliably detect gas exhaust in different scenarios, making it appealing for offline pre-labeling and online applications such as ghost object detection.Comment: Accepted for ITSC202

Research on human immunodeficiency virus (HIV) in Malawi: the Johns Hopkins University- Ministry of Health (JHU-MOH) project

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Performance of Semi-dwarf Apple Rootstocks in Two-dimensional Training Systems

In 2014, an intensive multileader apple rootstock orchard trial was established in Trento province, Northern Italy, using dwarf ('M.9-T337') and semidwarf rootstocks ('G.935', 'G.969', and 'M.116') and 'Gala', 'Golden Delicious', and 'Fuji' as the scion cultivars. Trees were trained to Biaxis ('M.9-T337') and Triaxis systems ('G.935', 'G.969', and 'M.116') with a tree density of 3175 trees and 2116 trees per hectare, respectively, and with a uniform axis (leader) density of 6348/ha. Comparisons across all training systems by cultivar system showed that after 6 years (2019), trees of 'Fuji' and 'Golden Delicious' on 'M.116' were the largest trees followed by 'G.969', 'G.935', and 'M.9-T337'. With 'Gala', trees on 'G.969' were of similar size as trees on 'M.116' and 'G.935'. Trees of 'Fuji' on 'G.935' produced the highest yield followed by 'G.969', 'M.116', and 'M.9-T337'. For 'Gala', trees on 'M.116' produced similarly as the 'M.9-T337', whereas with 'Golden Delicious', 'G.969' and 'G.935' had higher yields than 'M.9-T337'. When comparing production per ground surface area (hectare) 'G935' had higher yield than 'M.9-T337' for all the cultivars in this trial. In addition, yield efficiency of 'Fuji' trees on 'G.935' was similar or even higher than trees on 'M.9-T337'. Rootstock did not affect fruit size with 'Fuji'. For Gala, fruit from 'G.969' were significantly larger than those on 'M.116'. 'Golden Delicious' on 'G.969' produced smaller fruit compared with those on 'G.935'. Fruit from trees on 'M.9-T337' had the lowest percentage of red color with 'Fuji' and the highest with 'Gala'. When yield and quality data were combined to produce marketable yield, rootstock had a dramatic effect on the cumulative gross crop value per hectare based on local farm gate values for each scion cultivar

Feasible, efficient and necessary, without exception - Working with sex workers interrupts HIV/STI transmission and brings treatment to many in need

Background and Overview. High rates of partner change in sex work-whether in professional, 'transactional' or other context-disproportionately drive transmission of HIV and other sexually transmitted infections. Several countries in Asia have demonstrated that reducing transmission in sex work can reverse established epidemics among sex workers, their clients and the general population. Experience and emerging research from Africa reaffirms unprotected sex work to be a key driver of sexual transmission in different contexts and regardless of stage or classification of HIV epidemic. This validation of the epidemiology behind sexual transmission carries an urgent imperative to realign prevention resources and scale up effective targeted interventions in sex work settings, and, given declining HIV resources, to do so efficiently. Eighteen articles in this issue highlight the importance and feasibility of such interventions under four themes: 1) epidemiology, data needs and modelling of sex work in generalised epidemics; 2) implementation science addressing practical aspects of intervention scaleup; 3) community mobilisation and 4) the treatment cascade for sex workers living with HIV. Conclusion. Decades of empirical evidence, extended by analyses in this collection, argue that protecting sex work is, without exception, feasible and necessary for controlling HIV/STI epidemics. In addition, the disproportionate burden of HIV borne by sex workers calls for facilitated access to ART, care and support. The imperative for Africa is rapid scale-up of targeted prevention and treatment, facilitated by policies and action to improve conditions where sex work takes place. The opportunity is a wealth of accumulated experience working with sex workers in diverse settings, which can be tapped to make up for lost time. Elsewhere, even in countries with strong interventions and services for sex workers, an emerging challenge is to find ways to sustain them in the face of declining global resources

STD/HIV control in Malawi and the search for affordable and effective urethritis therapy: a first field evaluation.

OBJECTIVES--To evaluate gonococcal (GU) and nongonococcal urethritis (NGU), chlamydia antigen, and serostatus for syphilis and human immunodeficiency virus (HIV) among males attending a Malawian STD clinic with complaints of urethral discharge and/or dysuria. To collect demographic and behavioural data and to determine the effectiveness of five treatments for urethritis. METHODS--Urethritis was diagnosed using microscopy and culture for Neisseria gonorrhoeae. Sera were screened with rapid plasma reagin (RPR) and if reactive, with microhaemagglutination for Treponema pallidum (MHA-TP). HIV antibodies and chlamydia antigen were detected using enzyme immunoassay. Patients were randomised for treatment, cure was assessed 8-10 days later. RESULTS--At enrolment, GU was diagnosed in 415 (80.3%) and NGU in 59 (11.2%) of 517 males. Chlamydia antigen was found in 26 (5.2%) of 497 specimens tested. Syphilis seropositivity rate (RPR and MHA-TP reactive) was 10.7%. Overall HIV seroprevalence was 44.2%; 71.7% of men with reactive syphilis serology were HIV(+) compared with 40.9% of syphilis seronegatives (OR: 3.6, p < 0.001). Trimethoprim 320 mg/sulphamethoxazole 1600 mg by mouth for 2 days (TMPSMX), or the combination of amoxicillin 3 gm, probenicid 1 gm, and clavulanate 125 mg by mouth once (APC), failed to cure gonorrhoea effectively. Amoxicillin 3 gm, probenicid 1 gm, and clavulanate 125 mg, by mouth once with doxycycline 100 mg BID for 7 days (APC-D), gentamicin 240 mg IM once (GENT), ciprofloxacin 250 mg by mouth once (CIPRO) cured 92.9% to 95% of gonorrhoea. APC-D treatment did not generate less NGU at follow-up. HIV serostatus did not affect cure of urethritis. CONCLUSION--All patients presenting with urethritis should be treated syndromically using a simple algorithm and screened for syphilis seroreactivity for appropriate treatment and counselling