A computational model for path loss in wireless sensor networks in orchard environments.

A computational model for radio wave propagation through tree orchards is presented. Trees are modeled as collections of branches, geometrically approximated by cylinders, whose dimensions are determined on the basis of measurements in a cherry orchard. Tree canopies are modeled as dielectric spheres of appropriate size. A single row of trees was modeled by creating copies of a representative tree model positioned on top of a rectangular, lossy dielectric slab that simulated the ground. The complete scattering model, including soil and trees, enhanced by periodicity conditions corresponding to the array, was characterized via a commercial computational software tool for simulating the wave propagation by means of the Finite Element Method. The attenuation of the simulated signal was compared to measurements taken in the cherry orchard, using two ZigBee receiver-transmitter modules. Near the top of the tree canopies (at 3 m), the predicted attenuation was close to the measured one-just slightly underestimated. However, at 1.5 m the solver underestimated the measured attenuation significantly, especially when leaves were present and, as distances grew longer. This suggests that the effects of scattering from neighboring tree rows need to be incorporated into the model. However, complex geometries result in ill conditioned linear systems that affect the solver's convergence.

Fruit surface temperature data at different ripeness stages and ambient temperature provided as temperature-annotated 3D point clouds of apple trees.

By means of a unique, low vibration circular conveyor system, plant sensors capturing light detection and ranging (LiDAR) unit and thermal camera were moved on the same route, around blocks of apple trees, with seven Malus x domestica Borkh. 'Gala' apple trees in each block. Measurements took place four times during the season. Additionally at harvest, diurnal courses were recorded with 18 readings during three days. The data are provided as [i] raw data (3D point clouds of 3 blocks of trees scanned from right and left sides and thermal images), [ii] processed 3D point clouds of canopies annotated with temperature data from the thermal camera, and [iii] manually segmented 3D point clouds of fruit, representing the spatially-resolved fruit surface temperature (FST). Manual FST readings are provided on each measuring date and during diurnal courses. The fruit data are capturing 1236 FST, providing temperature distribution as 3D point cloud and one manually recorded reference FST per fruit. Additionally, fruit size and colour were measured for each fruit, despite for the first date, when fruit were too small for colour readings. Weather data are provided from a station located in the orchard. Usage of data could be (a) in developing methodology for 3D point cloud processing based on raw data, accomplished with reference FST data. Furthermore, (b) the pre-processed point clouds of fruit surface temperature can be reused in ecophysiological studies related to global warming, optimizing fruit production systems, and other. Because the sensors and trees were measured from the same angle and distance, time series analysis of the canopies would be possible.