Abstract: The applanation system on air permeated screen (1) of this invention is a versatile instrument for measurement of tissue firmness generated by cell turgor pressure of leaves and organs segments with thin and elastic cell wall of several stalk, leaves, fruits and roots. It is composed by an air source with adjusted maximum flow (6), a porous element (2) inserted on a screen (1), by which air flows, and a device for compression application (4). For measurements, the organ is progressively pressed against the screen until reversible interruption of the air flow crossing the porous element (2) is indicative of adequate applanation of the leaf against the screen. In this condition, one takes the firmness measurement reading as the applanation pressure (5). This firmometer enables postharvest quality evaluation and measurements for ecophysiology studies and irrigation management procedure benchmarking.

Abstract: The present invention refers to a computer vision system for identifying the fungus Guignardia citricarpa, the causing agent of the citrus black spot. The invention refers to a method for identifying Guignardia citricarpa using a computer vision system comprising the steps of a) image acquisition from a collection disk using a digital camera connected to a microscope b) pre-processing to improve (or correct) the scanned images c) image segmentation to segregate each particle in the image d) analysis and extraction of relevant features of the segmented particles and e) identification using artificial intelligence techniques and artificial neural networks.

Abstract: The present invention refers to a computer vision system for identifying the fungus Guignardia citricarpa, the causing agent of the citrus black spot. The invention refers to a method for identifying Guignardia citricarpa using a computer vision system comprising the steps of a) image acquisition from a collection disk using a digital camera connected to a microscope b) pre-processing to improve (or correct) the scanned images c) image segmentation to segregate each particle in the image d) analysis and extraction of relevant features of the segmented particles and e) identification using artificial intelligence techniques and artificial neural networks.