The application of new technologies in precision agriculture offers the possibility to link information to very specific crop locations. The spatial representation of these agricultural data through yield and fruit quality maps allows for crop management in a precise way that means that agricultural operations may be carried out considering intraorchard variability, thus resulting in greater efficiency. The aim of this work was to advance the development of new tools for the generation of yield and quality maps for precision agriculture. A new tool was implemented for citrus through a dashboard called CitrusYield that integrates the requirements demanded by technicians and farmers in terms of spatial distribution and the quality of their citrus production. The data for testing were collected by a prototype of a citrus harvest-assist platform. In order to obtain maps showing heterogeneity of production, an experimental plot with different varieties and variable production was chosen. The maps, tables and graphs showing different crop data were generated automatically by CitrusYield from the analysis of the data collected. The main advantage of knowing the differences in production between the swaths and areas inside the crop is to provide the grower with precise information to make accurate decisions for localised crop management, such as fertilisation, irrigation, pest and disease control, etc.

Tetranychus urticae Koch is an important citrus pest that produces chlorotic spots on the leaves and scars on the fruit of affected trees. It is detected by visual inspection of the leaves. This work studies the potential of colour and hyperspectral imaging (400–1000 nm) under laboratory conditions as a fast and automatic method to detect the damage caused by this pest. The ability of a traditional vision system to differentiate this pest from others, such as Phyllocnistis citrella, and other leaf problems such as those caused by nutritional deficiencies, has been studied and compared with a more advanced hyperspectral system. To analyse the colour images, discriminant analysis has been used to classify the pixels as belonging to either a damaged or healthy leaves. In contrast, the hyperspectral images have been analysed using PLS DA. The rate of detection of the damage caused by T. urticae with colour images reached 92.5%, while leaves that did not present any damage were all correctly identified. Other problems such as damage by P. citrella were also correctly discriminated from T. urticae. Moreover, hyperspectral imaging allowed damage caused by T. urticae to be discriminated from healthy leaves and to distinguish between recent and mature leaves, which indicates whether it is a recent or an older infestation. Furthermore, good results were achieved in the discrimination between damage caused by T. urticae, P. citrella, and nutritional deficiencies.