Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that simulation, prediction, and multi-modal integration are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity. Research in this direction has brought extensive empirical evidence, suggesting that Internal Models are suitable mechanisms for sensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints. We propose the Self-Organized Internal Models Architecture (SOIMA), a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensorymotor schemes. Our approach addresses integrally the issues of current implementations of Internal Models. We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.

Response to my critics Response to NussbaumLet me begin by indicating that I too am deeply concerned with the oppression and marginalization experienced by other groups besides ethnocultural groups. If I did not discuss 'dispersed groups' (to use the term that Nussbaum adopts to refer to subgroups within ethnocultural populations) -such as gays and lesbians, women, sex workers, and so forth -it was not because I do not think that this is an important topic. It was because, first, one cannot do everything at once. I wanted to give the complex issues raised by ethnocultural diversity the detailed attention that they deserve. Too many authors gloss over important differences between ethnocultural groups and as a result develop theories that are inadequate for understanding the political implications of ethnocultural diversity. Second, and more important, I focused on ethnocultural groups because they represent the most compelling cases challenging the political legitimacy of existing states. Unlike dispersed groups, some ethnocultural groups formed autonomous communities with their own sociopoliticial and economic institutions before their coerced incorporation into the state. This forced incorporation represents a fundamental challenge to conventional normative theories of the state. In addition, the issues with which I am concerned when I discuss self-determination, such as democratic empowerment and universal property rights, are intended to apply to, and empower, all members of ethnocultural groups, including oppressed subgroups within the latter.What I want to do here is to show that in the book I adopt positions that already address many of Nussbaum's concerns regarding dispersed groups. She makes three observations about ethnocultural groups and notes that there are four dangers that we can identify based on these observations. I will discuss these three observations and four dangers in turn.

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