Plant layout and pick-and-place strategies for improving performances in secondary packaging plants of food products

The aim of secondary packaging plants is to pick food products from a conveyor belt and to place them into boxes. The typical configuration of these packaging plants consists of a set of sequential robot stations, performing pick and place cycles from one conveyor to another parallel one, which transport the products and the boxes to be filled. Depending on the relative movement of the two conveyors, the plant operates in co-current or counter-current flow configuration. Undesired perturbations in the product flow rate from its nominal value can lead to critical events, i.e. unpicked product at the end of the first conveyor or not-completely filled boxes. Even if the structures of co-current flow and of counter-current flow plants, are very similar, their behaviour in non-nominal or perturbed conditions can be significantly different. The aim of this paper is to deeply investigate the behaviour of these two kinds of secondary packaging lines, evaluating their performances in the case of different pick and place strategies, using discrete events simulation techniques. Results show to which extent the different proposed control strategies can improve the performances of both co-current and counter-currents plants and, in particular, how co-current plant layouts can achieve performances which are equivalent to, or perhaps even better than, those that can be obtained with a counter-current plant layout, that cannot be freely used since it has been patented. The simulation tool, control algorithms and results presented can help packaging plant designers for choosing the most appropriate solutions and for properly sizing the plant. Copyright © 2012 John Wiley & Sons, Ltd

Application of Computer Vision for quality control in frozen mixed berries production: colour calibration issues

Computer vision is becoming increasingly important in quality control of many food processes. The appearance properties of food products (colour, texture, shape and size) are, in fact, correlated with organoleptic characteristics and/or the presence of defects. Quality control based on image processing eliminates the subjectivity of human visual inspection, allowing rapid and non-destructive analysis. However, most food matrices show a wide variability in appearance features, therefore robust and customized image elaboration algorithms have to be implemented for each specific product. For this reason, quality control by visual inspection is still rather diffused in several food processes. The case study inspiring this paper concerns the production of frozen mixed berries. Once frozen, different kinds of berries are mixed together, in different amounts, according to a recipe. The correct quantity of each kind of fruit, within a certain tolerance, has to be ensured by producers. Quality control relies on bringing few samples for each production lot (samples of the same weight) and, manually, counting the amount of each species. This operation is tedious, subject to errors, and time consuming, while a computer vision system (CVS) could determine the amount of each kind of berries in a few seconds. This paper discusses the problem of colour calibration of the CVS used for frozen berries mixture evaluation. Images are acquired by a digital camera coupled with a dome lighting system, which gives a homogeneous illumination on the entire visible surface of the berries, and a flat bed scanner. RBG device dependent data are then mapped onto CIELab colorimetric colour space using different transformation operators. The obtained results show that the proposed calibration procedure leads to colour discrepancies comparable or even below the human eyes sensibility

Cooperative Agricultural Operations of Aerial and Ground Unmanned Vehicles

Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Autonomous ground and aerial vehicle can lead to favorable improvements in management by performing in-field tasks in a time-effective way. Greater benefits can be achieved by allowing cooperation and collaborative action among Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). A multi-phase approach is here proposed, where each unmanned vehicle involved has been conceived and will be designed to implement innovative solutions for automated navigation and infield operations within a complex irregular and unstructured scenario as vineyards in sloped terrains

Cooperation of unmanned systems for agricultural applications: A theoretical framework

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here

Energy performance and climate control in mechanically ventilated greenhouses: A dynamic modelling-based assessment and investigation

Controlled environment agriculture in greenhouse is a promising solution for meeting the increasing food demand of world population. The accurate control of the indoor environmental conditions proper of greenhouses enhances high crop productivity but, contemporarily, it entails considerable energy consumption due to the adoption of mechanical systems. This work presents a new modelling approach for estimating the energy consumption for climate control of mechanically ventilated greenhouses. The novelty of the proposed energy model lies in its integrated approach in simulating the greenhouse dynamics, considering the dynamic thermal and hygric behaviour of the building and the dynamic response of the cultivated crops to the variation of the solar radiation. The presented model simulates the operation of the systems and the energy performance, considering also the variable angular speed fans that are a new promising energy-efficient technology for this productive sector. The main outputs of the model are the hourly thermal and electrical energy use for climate control and the main indoor environmental conditions. The presented modelling approach was validated against a dataset acquired in a case study of a new fully mechanically controlled greenhouse during a long-term monitoring campaign. The present work contributes to increase the knowledge about the dynamics and the energy consumption of greenhouses, and it can be a valuable decision support tool for industry, farmers, and researchers to properly address an energy efficiency optimisation in mechanically ventilated greenhouses to reach the overall objective of decreasing the rising energy consumption of the agricultural sector

Automatic Path Planning for Unmanned Ground Vehicle Using UAV Imagery

Field machines play an important role in the management of agricultural environments. Increasing use of automated machines in precision agriculture has gained significant attention of farmers and industries to minimize human work load to perform tasks such as land preparation, seeding, fertilizing, plant health monitoring and harvesting. Path planning is considered as a fundamental step for agricultural machines equipped with autonomous navigation system. For mountain vineyards, path planning is a big challenge due to terrain morphology and unstructured vineyards. This paper proposes a workflow to generate an automatic coverage path plan for unmanned ground vehicles (UGVs) using georeferenced imagery taken by an unmanned aerial vehicle (UAV). First, image acquisition is performed over a vineyard to generate an orthomosaic and a digital surface model, which are then used to identify the vine rows and inter-row terrain. This information is then used by the algorithm to generate a path plan for UGV