This paper proposes a sequential masking algorithm based on the K-means method that combines RGB and multispectral imagery for discrimination of Cabernet Sauvignon grapevine elements in unstructured natural environments, without placing any screen behind the canopy and without any previous preparation of the vineyard. In this way, image pixels are classified into five clusters corresponding to leaves, stems, branches, fruit and background. A custom-made sensory rig that integrates a CCD camera and a servo-controlled filter wheel has been specially designed and manufactured for the acquisition of images during the experimental stage. The proposed algorithm is extremely simple, efficient, and provides a satisfactory rate of classification success. All these features turn out the proposed algorithm into an appropriate candidate to be employed in numerous tasks of the precision viticulture, such as yield estimation, water and nutrients needs estimation, spraying and harvesting.

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The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.

The inspection platform that is described in this manuscript consists of a hexapod walking robot designed by the Centre for Automation and Robotics CSIC-UPM, Spain. This inspection platform will load a scanning manipulator arm which, in turn, carries a metal detector on its tool centre point. With the integration of both, hexapod robot and scanning manipulator, several test tasks about the search and localisation of antipersonnel mines will be carried out, within a controlled environment. The SCARA configuration of the hexapod robot legs will allow low energy consumption when the robot executes gaits on flat terrain or with reduced slope, due the decoupling of gravitational effects. This legged robot has a mass about 250 kg, and it can bear a high payload up to about 300 kg. Considering this load characteristic then the vibrational effects on the scanning manipulator will be reduced, when this carry out scanning tasks over the terrain.