1,720,964 research outputs found
On-line real-time fruit size estimation using a depth-camera sensor
Full text linkFruit weight is one of the factors taken into account when performing yield estimations together with the trees density and orchard's area. Thus, having the possibility to collect data about the weight of a large number of fruits in the orchard gives the possibility to increase the reliability of the yield estimation. Over recent years, mathematical models able to convert the fruit size into fruit weight were evaluated as effective. Since then, manual data collection with calipers and automated/continuous fruit gauges were tested to collect fruit size data to perform yield predictions. Their main drawbacks are respectively the need for human-labour, repetitiveness, being time-requiring and the limited sample varying from 20 to 200 fruits per hectare. This research is trying to discover and deepen the use of AI in agriculture for doing a step further: sizing fruits after their detection with a YOLOv5 Neural network algorithm. To reach this goal, a system which takes as a input RGB-D depth-camera's color images and 16 bit depth maps was developed. After applying YOLOv5 detection, two different methodologies (by mean of squared bounding boxes and circular shapes) to extract from the depth map the distance data needed to size the target object were tested. Results from a preliminary data-set showed that the system could be a potential solution to increase the sample dimension and perform yield prediction. The main drawbacks of the developed vision-system are related to the errors in sizing the objects, which are ranging from an underestimation of about 9 mm to an overestimation of 24 mm. From the initial results was possible to identify the squared-bbox-mediated sizing process as a better pathway rather than the one performed with circular-bboxes, since the RMSE is always smaller with values of 7–9 m
A computer vision system for in-field quality evaluation: preliminary results on peach fruit
No full textIn Italy, peaches are paid according to size, color
and appearance. Real time fruit harvest quality information
could support growers and the whole fruit chain improving
segmented selection for consumers as well as to increase
growers’ income. In this study, a computer vision system was
tested aiming to quantifying and sizing peaches in bins at
harvest time. Two different depth cameras the Intel RealSense
D435i and D455, and two different light conditions, natural and
artificial, were tested, to assess potential issues and to achieve
the most suitable set-up for future developments. Automated
fruit detection appeared less difficult, while the system presents
generally overestimation in fruit size. The D435i camera in
artificial light condition obtained the best outcome with a RMSE
of 17.91 mm, compared to the reference diameter of measured
fruit. Although the results obtained are with low accuracy and
precision, the vision systems technique seems promising and
suggests solutions to further improvements. Future studies will
focus on improving the system for sizing and color estimation,
coupled to georeferenced data directly in the field with the aim
of mapping field quality variability. The idea is to develop a lowcost
tool that coupled to harvesting platforms connects fruit
quality at the time of harvest to post-harvest operations
Development of a consumer-grade scanning platform for fruit thermal and position data collection
Full text linkClimate change and more frequent heatwaves exacerbate the issue of fruit sunburn in orchards. To facilitate fruit temperature dynamics investigation, in relation to fruit sunburn damage occurrence, a low-cost thermal scanning platform, based on depth and thermal consumer-grade cameras, was developed to collect position and temperature fruit information. The platform exploits the Robotic Operating System (ROS) to synchronize data collection from the sensors, the YOLOv5 object detection algorithms to automatically detect fruits to be analyzed, and a Python based pipeline to align images and extract temperature and position information of the fruits (apple and grape cluster). Results referred to a first version of the system shown a high correlation between estimated and actual temperature (r>0.92) and an acceptable positional error (∼0.15 m). Many improvements of the system are currently on-going to reach the expected performance on a second version of the platform
On-line real-time trunk detection, counting and sizing to enable precision agriculture tasks on a single-plant basis
No full textTo facilitate autonomous operations in orchards, an
effective information management system is necessary. It should
gather and process data on crop performance, including yield
count, canopy volume, and crop health. A practical approach is
to structure the system by ’discretizing’ it to individual trees,
which requires tree segmentation/detection as a key component.
This enables precise monitoring and analysis of each tree’s
condition and productivity, aiding in informed decision-making
and optimized orchard operations. The presented study wants
to develop a low-cost approach to trunk detection, counting
and sizing to possibly enable such an informed knowledgebase
and decision making orchard management. The system
relies on traditional computer vision algorithm to enable trunk
segmentation by exploiting color and depth image information.
Preliminary results are provided
OpenAcces_RGBD_apple_dataset
No full textIntel realsense d435i open access dataset of seasonal growth of fuji apple. The dataset contains images and reference caliper ground truth data. Data were collected during 2022 season in a 3 years old apple orchard trained as 'Planar Cordon' (bidimensional training system). 12 fruit on two trees (24 fruit in total) were monitored for their fruit size along the whole season. RGB-D pictures, manually labelled for the monitored fruit, were taken on 17 different dates from a fruit size of 40mm approx. to >80mm approx
For more detailed info check the 'data_exploration' Jupyter notebook in the notebook folde
Mixing supervised and unsupervised learning algorithms to solve human perception subjectivity in internal fruit quality assessment
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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