Research and development in agricultural robots are continuously increasing. However, dynamically changing agricultural environments provide adverse conditions to robotics operability. In order to perform the agricultural tasks safely and accurately, reliable landmarks from the surrounding environment need to be identified. In this work, deep learning is employed for accurate and fast detection of high-level features of vineyards, the vine trunks. More specifically, Faster regions-convolutional neural network (Faster R-CNN), You Only Look Once version 3 (YOLOv3) and YOLOv5 are tested for real-time vine trunk detection. The models are trained with an in-house dataset designed for the needs of this study, containing 1927 annotated vine trunks in 899 different images. Comparative results indicate YOLOv5 as the configuration that allows the faster and most accurate vine trunk detection, achieving an overall Average Precision of 73.2% in 29.6 ms. The high precision combined with the fast runtime performance prove that the YOLOv5 detector is suitable for real-time vine trunk detection executed by an autonomous harvesting robot.
Industrial product quality is frequently assessed using up to second-order statistics of populations of measurements. Lately, a fuzzy interval number (FIN) was used for representing a whole population of samples. It turns out that a FIN can asymptotically capture statistics of all orders. The space F of FINs, including both conventional (fuzzy) numbers and conventional intervals, is studied here. A FIN is interpreted as a (linguistic) information granule that can capture industrial ambiguity. Based on generalized interval analysis it is shown rigorously that F is a metric mathematical lattice; moreover it is shown that F a cone in a linear space. An enhanced extension of Kohonen's Self-Organizing Map (KSOM), namely granular SOM or grSOM for short, is presented in FN for inducing a distribution of FINs from populations of measurements. The grSOM produces descriptive decision-making knowledge (i.e. rules) from the training data by expert attaching labels to induced n-tuples of FINs. Generalization is feasible beyond rule support. A positive valuation function, computable genetically, can introduce tunable nonlinearities. Preliminary results are demonstrated regarding industrial fertilizer quality assessment. Fuzzy-mathematical-morphology-based image processing techniques, which combine binary thresholding and object recognition, are used to automatically measure the geometry of fertilizer granules. Additional measurements are also considered. The far-reaching practical potential of the proposed techniques is discussed.
Conference Committee Involvement (1)
Mathematical Methods in Pattern and Image Analysis
3 August 2005 | San Diego, California, United States
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