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Managing orchards requires delicate agricultural operations being typically carried out in narrow zones where the operators usually drive machineries under stress that could result in poor performance. In such conditions, the use of technology would help manage the machines to reduce the hazardous work and eventual damage to the plants. To safely drive a tractor, the driver needs to be aware of its surroundings, thus a stereovision system can provide helpful information. Stereo imaging has proven to be an effective three-dimensional vision system. Indeed, the range (or third coordinate) information is useful to detect the obstacle distances. Such distances, when detected during agricultural operations, could be used to assist the operator in driving the tractor at regular or variable working speeds and eventually to provide manufacturers useful indications to model the form of ROPS (roll over protection structure). This study aimed to verify the closeness of agreement between manual and stereo-image measurements, and thus to provide helpful information regarding safety and working purposes. The system used a custom low-cost dual web-camera in combination with an image analysis algorithm in order to automatically extract the information needed. Manual independent measurements were carried out using a metric tape (sensitivity 1 cm). A regular structure was used for the analysis: four rows of ten trees each one. Alternated red and blue paper markers were placed on the hazelnut trees (two per tree) of two couples of rows for enhanced visibility. For each couple of trees (one on the right, the other on the left), the four markers formed a trapezoid that was measured. The results of the analysis demonstrated that the stereo vision provided distance measurements with reasonable accuracy (error <5%) in the range of distances lower than 20 m. The resolution assessed for the developed video system is suitable for obtaining distance information in real scenes. This information could be used to assist drivers to operate agricultural machineries through narrow tree rows during work execution. Moreover, such information could be used for safeguarding decision-making and/or for controlling some tractor functions such as continuing moving, changing driving direction, changing 3-point hitch position, reducing transmission speed, halting the tractor. These functions will be necessary before tractors become fully autonomous. Finally, the measured distances, marking the narrow transitions between the tree rows, could be also used to study the ROPS form, both for working safely and for avoiding possible damage caused to the hazel trees laterally.

One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149-1:2010). Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres) was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm); the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang's theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors) and size descriptors (area, perimeter, Feret diameters, eccentricity) was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3-dimensional acquisition process. IntroductionThe size distribution of wood chips is recognized as one of the most important parameters for efficient combustion since it affects the storage, the efficiency of energy conversion and environmental emissions (Nati et al., 2010). The dimensions of wood chips are specified by the international standard EN 14961-1:2010 (CSN, 2010) ( Table 1).For fuel chips, mechanically or manually operated screening devices are commonly applied and there is a large variety of applicable systems. The reference methods for size classification of samples are EN 15149-1:2010 (CSN, 2010; oscillating screen method) and CEN/TS 15149-3:200615149-3: (CNS, 2006; rotary screen method). The average relative repeatability limits for horizontal (< 2 w-%) and rotary screenings are exceedingly low. Generally, the relative reproducibility limits based on the median values for horizontal (< 10 w-%) and rotary screening results seem acceptable. On average, reproducibility is better for horizontal screenings than for rotary screening. However, if different measuring principles are applied, particle size analysis of biofuels is associated with high measuring uncertainties (Hartmann et al., 2006).Since the chip form (shape and size) influences the sieve results, the...

One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010). Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres) was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm); the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors) and size descriptors (area, perimeter, Feret diameters, eccentricity) was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process.