Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201

Texture Segregation in Chromatic Element-Arrangement Patterns

We compare the perceived segregation of element-arrangement patterns1 which are composed of two types of squanes arranged in vertical stripes in the top and bottom regions and in a checkerboard in the middle region. The squares in a pattern are either equal in luminance and differing in hue or equal in hue and differing in luminance. Perceived segregation of squares differing in hue is not predicted by their rated similarity, but rather by the square-root of the sum of the squares of the differences in the outputs of the L-M and L+M-S opponent channels. Adaptation to the background luminance affects judgements of perceived segregation but does not affect judgments of perceived similarity. For a given background luminance, perceived segregation is a linear function of cone contrasts. Perceived hue similarity is instead a linear function of cone excitations across the background luminances. High and low luminance backgrounds decrease the perceived segregation of patterns differing in luminance. A high luminance achromatic background decreases the perceived segregation of patterns differing in hue but a low luminance achromatic background does not. The results indicate that the adaptation luminance affects the contribution of luminance differences between the two types of squares to perceived segregation but not the contribution of hue differences. For element-arrangement patterns composed of squares of equal luminance that differ in hue, perceived segregation is associated with differences in the perceived brightness of the hues. The results are consistent with the findings that the perceived segregation in element-arrangement patterns is primarily a function of the early visual mechanisms that encode pattern differences prior to the specification of the forms of the squares and their properties.Office of Naval Research (N00014-91-J-4100, N00014-94-1-0597, N00014-95-1-0409); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0334); National Science Foundation (IIU-94-01659