Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation

The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_SizingThis work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU. We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition, and Pieter van Dalfsen and Dirk de Hoog from Wageningen University & Research for additional data collection used in the case study.info:eu-repo/semantics/publishedVersio

Characterization of the Ac/Ds behaviour in transgenic tomato plants using plasmid rescue

We describe the use of plasmid rescue to facilitate studies on the behaviour of Ds and Ac elements in transgenic tomato plants. The rescue of Ds elements relies on the presence of a plasmid origin of replication and a marker gene selective in Escherichia coli within the element. The position within the genome of modified Ds elements, rescued both before and after transposition, is assigned to the RFLP map of tomato. Alternatively to the rescue of Ds elements equipped with plasmid sequences, Ac elements are rescued by virtue of plasmid sequences flanking the element. In this way, the consequences of the presence of an (active) Ac element on the DNA structure at the original site can be studied in detail. Analysis of a library of Ac elements, rescued from the genome of a primary transformant, shows that Ac elements are, infrequently, involved in the formation of deletions. In one case the deletion refers to a 174 bp genomic DNA sequence immediately flanking Ac. In another case, a 1878 bp internal Ac sequence is deleted

On publicness theory and its implications for supply chain integration:The case of criminal justice supply chains

The literature has extensively discussed whether and how public organizations differ from private ones. Publicness theory argues that the degree of publicness is determined by ownership, funding, goal setting, and control structure of an organization. However, these theoretical ideas have not yet been extended to the interorganizational level. The need for further research is reflected in the sustained debate on the applicability of for-profit management approaches in public contexts and supply chains. Starting from the premise of the dimensional publicness theory, this study focuses on theory elaboration. We focus our empirical study on the criminal justice supply chain, which encompasses the process of bringing a criminal case to court. This chain provides an interesting public case to explore how specific dimensions of publicness affect or limit supply chain integration mechanisms. The results of our series of embedded cases focusing on Dutch criminal justice supply chains show that control structures, embodied in laws and regulations, define the governance of relationships between supply chain partners. In addition to these formalized ties, extensive known for-profit information and operational integration mechanisms can be observed, along with limited relational integration. Surprisingly, although similar integration mechanisms are used as in for-profit contexts, integration serves a different role in several of the relationships investigated: dealing with tensions stemming from the specific goal setting and stakeholders of criminal justice chains. Although our findings specifically relate to criminal justice supply chains, they have important implications for other supply chains using contracts and laws and those being selective in applying supply chain integration in cases of contrasting objectives. Moreover, we provide a stepping-stone for the extension of publicness theory to the interorganizational level

Climate sensitivity of shrub growth across the tundra biome

The tundra biome is experiencing rapid temperature increases that have been linked to a shift in tundra vegetation composition towards greater shrub dominance. Shrub expansion can amplify warming by altering the surface albedo, energy and water balance, and permafrost temperatures. To account for these feedbacks, global climate models must include realistic projections of vegetation dynamics, and in particular tundra shrub expansion, yet the mechanisms driving shrub expansion remain poorly understood. Dendroecological data consisting of multi-decadal time series of annual growth of shrub species provide a previously untapped resource to explore climate-growth relationships across the tundra biome. We analysed a dataset of approximately 42,000 annual growth records from 1821 individuals, comprising 25 species from eight genera, from 37 arctic and alpine sites. Our analyses demonstrate that the sensitivity of shrub growth to climate was (1) heterogeneous across the tundra biome, (2) greater at sites with higher soil moisture and (3) strongest for taller shrub species growing at the northern or upper elevational edge of their range. Across latitudinal gradients in the Arctic, climate sensitivity of growth was greatest at the boundary between low- and high-arctic vegetation zones, where permafrost conditions are changing and the majority of the global permafrost soil carbon pool is stored. Thus, in order to more accurately estimate feedbacks among shrub change, albedo, permafrost thaw, carbon storage and climate, the observed variation in climate-growth relationships of shrub species across the tundra biome will need to be incorporated into earth system models.JRC.H.3-Forest Resources and Climat

Complexity revealed in the greening of the Arctic

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This 'greening of the Arctic' is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change

Perineal wound closure using gluteal turnover flap or primary closure after abdominoperineal resection for rectal cancer: study protocol of a randomised controlled multicentre trial (BIOPEX-2 study)

BACKGROUND: Abdominoperineal resection (APR) for rectal cancer is associated with high morbidity of the perineal wound, and controversy exists about the optimal closure technique. Primary perineal wound closure is still the standard of care in the Netherlands. Biological mesh closure did not improve wound healing in our previous randomised controlled trial (BIOPEX-study). It is suggested, based on meta-analysis of cohort studies, that filling of the perineal defect with well-vascularised tissue improves perineal wound healing. A gluteal turnover flap seems to be a promising method for this purpose, and with the advantage of not having a donor site scar. The aim of this study is to investigate whether a gluteal turnover flap improves the uncomplicated perineal wound healing after APR for rectal cancer. METHODS: Patients with primary or recurrent rectal cancer who are planned for APR will be considered eligible in this multicentre randomised controlled trial. Exclusion criteria are total exenteration, sacral resection above S4/S5, intersphincteric APR, biological mesh closure of the pelvic floor, collagen disorders, and severe systemic diseases. A total of 160 patients will be randomised between gluteal turnover flap (experimental arm) and primary closure (control arm). The total follow-up duration is 12 months, and outcome assessors and patients will be blinded for type of perineal wound closure. The primary outcome is the percentage of uncomplicated perineal wound healing on day 30, defined as a Southampton wound score of less than two. Secondary outcomes include time to perineal wound closure, incidence of perineal hernia, the number, duration and nature of the complications, re-interventions, quality of life and urogenital function. DISCUSSION: The uncomplicated perineal wound healing rate is expected to increase from 65 to 85% by using the gluteal turnover flap. With proven effectiveness, a quick implementation of this relatively simple surgical technique is expected to take place. TRIAL REGISTRATION: The trial was retrospectively registered at Clinicaltrials.gov NCT04004650 on July 2, 2019

Ontwikkeling en validatie van computer vision technologie ten behoeve van een broccoli oogstrobot

De selectieve en handmatige oogst van broccoli is arbeidsintensief en omvat ongeveer 35% van de totale productiekosten. Dit onderzoek is uitgevoerd om te bepalen of computer vision kan worden gebruikt om broccoli kronen te detecteren, als eerste stap in de ontwikkeling van een autonome selectieve broccoli oogstrobot. Een op textuur en kleur gebaseerde beeld detectie is gebruikt om de broccoli kronen van de achtergrond te scheiden. De computer vision is gevalideerd met een ground truth dataset van 200 afbeeldingen. In deze beelden zijn 228 werkelijke broccoli kronen van verschillende groottes aangewezen door twee menselijke experts gebruikmakend van het GrabCut-algoritme. De broccoli detectie van de computer vision is op twee verschillende manieren beoordeeld. De eerste was een pixel-gebaseerde overlap tussen de computer vision en de werkelijke broccoli objecten, wat resulteerde in een gemiddelde overlap van 93.8%. De tweede waarde was op basis van de detectie van de individuele broccoli kronen. Deze toonde een precisie van 99.5%, met een slechts één onterecht aangemerkte broccoli. De specificiteit was 97.9%, de negative predictive value was 69.7% en de gemiddelde nauwkeurigheid was 92.4%. In het totaal zijn 208 broccoli kronen gedetecteerd door de computer vision, wat wijst op een sensitiviteit van 91.2%. De gemiddelde grootte van de gemiste kronen was kleiner dan de gemiddelde grootte van de gedetecteerde kronen. Indien de broccoli kronen slechter zichtbaar zijn of overschaduwd worden door omringende bladeren is het mogelijk dat de computer vision misclassificaties levert

Machine vision for a selective broccoli harvesting robot

The selective hand-harvest of fresh market broccoli is labor-intensive and comprises about 35% of the total production costs. This research was conducted to determine whether machine vision can be used to detect broccoli heads, as a first step in the development of a fully autonomous selective harvester. A texture and color based image segmentation was used to separate the broccoli head from the background. Segmentation results were compared to a ground truth dataset of 200 images. In these images, 228 broccoli heads of varying sizes were classified by two human experts with the GrabCut algorithm. Image segmentation was evaluated by two different metrics. The first was a pixel-based spatial overlap between the ground truth classification and image segmentation, which resulted an average overlap of 93.8%. The second metric was the individual broccoli head detection and the corresponding confusion matrix. These showed a precision score of 99.5%, indicating only one false positive. The specificity was 97.9%, negative predictive value was 69.7% and overall accuracy 92.4%. In total, 208 broccoli heads were detected by the machine vision software, indicating a sensitivity score of 91.2%. The average pixel size of the non-detected heads was smaller than the pixel size of the detected head