131 research outputs found
Open problems in traceability: from raw materials to finished food products
Even though the main EU regulations concerning food traceability have already entered to force since many years, we still remark very wide and impacting product recalls, which often involve simultaneously large territories and many countries. This is a clear sign that current traceability procedures and systems, when implemented with the only aim of respecting mandatory policies, are not effective, and that there are some aspects that are at present underestimated, and therefore should be attentively reconsidered. In particular, the sole adoption of the so-called “one step back-one step forward traceability” to comply the EC Regulation 178/2002, where every actor in the chain handles merely the data coming from his supplier and those sent to his client, is in fact not sufficient to control and to limit the impact of a recall action after a risk notification. Recent studies on lots dispersion and routing demonstrate that each stakeholder has to plan his activities (production, transformation or distribution) according to specific criteria that allow pre-emptively estimating and limiting the range action of a possible recall. Moreover, these new and very recently proposed techniques still present some limits; first of all the problem of traceability of bulk products (e.g. liquids, powders, grains, crystals) during production phases that involve mixing operations of several lots of different/same materials. In fact, current traceability practices are in most cases unable to deal efficiently with this kind of products, and, in order to compensate the lack of knowledge about lot composition, typically resort to the adoption of very large lots, based for instance on a considered production period. Aim of this paper is to present recent advances in the design of supply chain traceability systems, discussing problems that are still open and are nowadays subject of research
Cooperation of unmanned systems for agricultural applications: A case study in a vineyard
Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the
Agriculture 4.0 framework when operating within cooperative architectures, thanks to
their ability to tackle difficult tasks, particularly within complex irregular and unstructured
scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has
been proposed as an alternative to more common cooperative schemes. When perennial
crops are considered, it is advantageous to build a simplified geometrical (and georeferenced)
crops model, which can be identified by using 3D point clouds acquired during apriori
explorative missions by unmanned aerial vehicles. This model can be used to plan
the tasks to be performed within the crops by the in-field aerial and ground drones. In this
companion paper, the proposed strategy is applied to a specific case study involving a
vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc
technologies and guidance, navigation and control algorithms were designed and implemented.
The main objectives were to improve the autonomous driving capabilities of the
drones involved and to automate the process of retrieving low-complexity maps from the
data collected with preliminary remote sensing missions to make them available for the
autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle
within the vine rows. Preliminary results highlight the benefits achievable by exploiting the
tailored technologies selected and applied to improve each of the analysed mission phases
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Precision agriculture is considered to be a fundamental approach in pursuing
a low-input, high-efficiency, and sustainable kind of agriculture when
performing site-specific management practices. To achieve this objective, a
reliable and updated description of the local status of crops is required.
Remote sensing, and in particular satellite-based imagery, proved to be a
valuable tool in crop mapping, monitoring, and diseases assessment. However,
freely available satellite imagery with low or moderate resolutions showed some
limits in specific agricultural applications, e.g., where crops are grown by
rows. Indeed, in this framework, the satellite's output could be biased by
intra-row covering, giving inaccurate information about crop status. This paper
presents a novel satellite imagery refinement framework, based on a deep
learning technique which exploits information properly derived from high
resolution images acquired by unmanned aerial vehicle (UAV) airborne
multispectral sensors. To train the convolutional neural network, only a single
UAV-driven dataset is required, making the proposed approach simple and
cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as
a case study for validation purposes. Refined satellite-driven normalized
difference vegetation index (NDVI) maps, acquired in four different periods
during the vine growing season, were shown to better describe crop status with
respect to raw datasets by correlation analysis and ANOVA. In addition, using a
K-means based classifier, 3-class vineyard vigor maps were profitably derived
from the NDVI maps, which are a valuable tool for growers
Effect of different chemical compounds concentration in aqueous solution on UHF RFID readability
Case studies in food freezing at very low temperature
Freezing is one of the most widely used and effective processes to preserve foods shelf-life during long periods of time. This paper focuses on very low temperature freezing, and a thermal model, based on literature formulations, was developed to calculate the food freezing time considering several kinds of food, with different sizes, shapes and chemical composition. Moreover, once evaluated the food freezing time as a function of temperature and velocity of the cooling fluid, a chart reporting the food production rate, the freezing time and the cooling capacity was developed to properly design the freezing equipment in terms of optimal choice of the process and type of freezer
Improving agricultural drone localization using georeferenced low-complexity maps
To properly locate and operate autonomous vehicles for in-field tasks, the knowledge of their instantaneous position needs to be combined with an accurate spatial description of their environment. In agricultural fields, when operating inside the crops, GPS data are not reliable nor always available, therefore high-precision maps are difficult to be obtained and exploited for in-field operations. Recently, low-complexity, georeferenced 3D maps have been proposed to reduce their computationally demand without losing relevant crop shape information. In this paper, we propose an innovative approach based on the ellipsoid method that allows us to fuse the data collected by ultrasonic sensors and the information provided by the simplified map to improve the location estimation of an unmanned ground vehicle within crops. Then, this improved estimation of the vehicle location can be integrated with orientation data, merging it with those provided by other sensors as GPS and IMU, using classical filtering schemes
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