A Mobile Laboratory for Orchard Health Status Monitoring in Precision Farming
Ristorto, G.
Gallo, R.
Gasparetto, A.
Scalera, L.
Vidoni, R.
Mazzetto, F.
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How to Cite

Ristorto G., Gallo R., Gasparetto A., Scalera L., Vidoni R., Mazzetto F., 2017, A Mobile Laboratory for Orchard Health Status Monitoring in Precision Farming , Chemical Engineering Transactions, 58, 661-666.
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Nowadays, Precision Farming (PF) has been fully recognized for its potential capability to increase field yields, reduce costs and minimize the environmental impact of agricultural activities. The first stage of the PF management strategy is the collection of field and environmental data useful to obtain information about the crop health status in a field or among different fields and to operate suitably in each of these partitions (e.g., distribution of fertilizers, pesticide treatments, differential harvesting), according to a site-specific approach, depending on their actual needs.
A mobile vehicle (Bionic eYe Laboratory - ByeLab) has been developed to monitor and sense the health status of orchards and vineyards. The ByeLab is a tracked-bins carrier equipped with different sensors: two LiDARs to evaluate the shape and the volume of the canopy and six optical sensors to evaluate its chlorophyll content, thus to monitor the health state of the crops. Moreover, a RTK GNSS (Real Time Kinematic Global Navigation Satellite System) is used to geo-reference the acquired data and an IMU (Inertial Measurement Unit) to record the orientation of the ByeLab during the surveys.
In order to reproduce the three-dimensional maps of the shape and the health of the canopy, data-processing algorithms have been developed and customized for the application. The combined use of heterogeneous sensors and a deep analysis of the results permit to define an efficient methodology for the crop monitoring. A Matlab® routine has been implemented for the canopy volume reconstruction and the health status mapping. Moreover, a preliminary diagnostic algorithm exploiting both the LiDAR and the optical sensors information to detect early situations of plants stress has been conceived and implemented.
The aim of this paper is to disclose the data-processing algorithms exploited to obtain a precise canopy thickness reconstruction and an accurate vegetative-state mapping. The methodology used to validate the canopy thickness measurements and the vegetative state mapping is also presented. In particular, to validate the thickness results obtained with the mobile ByeLab, they have been compared with the acquisitions made with a fixed terrestrial Laser Scanner showing a good correlation.
A representation of the parcels monitored during the in-field surveys and of an entire orchard field is finally provided.
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