Abstract The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopy-atmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a cost-effective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R 2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.
Traceability was introduced about twenty years ago to face the worldwide spread of food safety crises. Traceability data flow associated with each lot of food products during any production and/or delivery phases can also be used to guarantee product authenticity. For this purpose, it is necessary to protect the data from cyber intrusions and, at the same time, to guarantee the integrity of the bond between the physical product and the data. Price grading related to quality perceivable or credence attributes attracts criminals to attempt item substitution fraud. Improved track and trace technologies supported by artificial intelligence (AI) could highly enhance systems’ capability to detect authenticity violations by product substitution. This paper proposes an innovative method based on AI, to reinforce traceability systems in detecting possible counterfeiting by product substitution. It is an item-based mass balance method that analyses the congruity of the traceability data flows not by using explicit (even stochastic) rules but by exploiting the learning capabilities of a neural network. The system can then detect suspect information in a traceability data flow, alerting a possible profit-driven crime. The AI-based method was applied to a pork slaughtering and meat cutting chain case study.
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.
