Optical fiber sensors (OFSs) have emerged as essential tools in the monitoring of physical, chemical, and bio-medical parameters in harsh situations due to their high sensitivity, electromagnetic interference (EMI) immunity, and long-term stability. However, the current literature contains scattered information in most reviews regarding individual sensing technologies or domains. This study provides a structured exploratory review in a novel inter-family analysis of both intrinsic and extrinsic configurations by analyzing more than 23,000 publications between 2019 and 2025 in five key domains: industry, medicine and biomedicine, environmental chemistry, civil/structural engineering, and aerospace. The analysis aims to critically discuss how functional principles/parameters and methods of interrogation affect the applicability of different OFS categories. The results reveal leading trends in the use of techniques like the use of fiber Bragg gratings (FBG) and distributed sensing in high-accuracy conditions or the rising role of extrinsic sensors in selective chemical situations and point out new approaches in areas like Artificial Intelligence (AI)- or Internet of Things (IoT)-integrated sensors. Further, this synthesis not only connects pieces of knowledge but also defines the technological barriers in terms of calibration cost and standardization: this provides strategic insight regarding future research and the scalability of industry deployment.

The Path-Planning problem is a basic issue in mobile robotics, in order to allow the robots to solve more complex tasks, for example, an exploration assignment in which the distance given by the planner is taken as a utility measure. Among the different proposed approaches, algorithms based on an exact cell decomposition of the environment are very popular. In this paper, we present a new algorithm for universal path planning in cell decomposition, using a raster scan method for computing the Exact Euclidean Distance Transform (EEDT) for each cell in the map. Our algorithm computes, for every cell in the map, the point sequence to the goal. For each sequence, the sub-goals are selected near to the vertices of the obstacles, reducing the total distance to the goal without post processing. At the end, we obtain a smooth path up to the goal without the need for post-processing. The paths are computed by visibility verification among the cells, exploiting the processing performed in the neighbouring cells.

Citrus fruits are the second most important crop worldwide. One of the most important tasks is sorting, which involves manually separating the fruit based on its degree of maturity, and in many cases, involves a task carried out manually by human operators. A machine vision-based citrus sorting system can replace labor work for the inspection of fruit sorting. This article proposes a vision system for citrus fruit sorting implemented on a dedicated and efficient Field Programmable Gate Array (FPGA) hardware architecture coupled with a mechanical sorting machine, where the FPGA performs fruit segmentation and color and size classification. We trained a decision tree (DT) using a balanced dataset of reference images to perform pixel classification. We evaluate the segmentation task using a pixel accuracy metric, defined as the ratio between correctly segmented pixels produced by a DT and the total pixels in the reference image segmented offline using Otsu’s thresholding algorithm. The balance between correctly classified images by color or size and their corresponding labels of that color and size evaluates the color and size classification algorithms. Considering these metrics, the system reaches an accuracy of 97% for fruit segmentation, 94% for color classification, and 90% for size classification, running at 60 fps.

The use of sensors to detect or measure ripening changes in fruit is a growing area of interest to the scientific community. Colorimeters are commonly employed for color and shade identification; however, their usage to measure the color parameters of fruit rinds based on image analysis can be expensive. This article presents a non-invasive and low-cost optoelectronic system for detecting color changes in oranges to predict the ripening stage. The system utilises a 1 W white LED as a light source, an RGB photodiode array, and two plastic optical fibres bundled in parallel to form the head of an extrinsic sensor. A microcontroller is employed for model integration and data acquisition. The evolution of the skin color of the fruit was monitored until over-ripeness was evident. The sensor was designed to detect the color changes; the CIE L * a * b * color difference between the optoelectronic device results and those obtained by colorimetry was 2.6–4.5. To predict the ideal conditions for fruit handling and determine the maturity level, a multilevel perceptron ANN was trained, achieving an accuracy of 96.4%. In addition, an overall precision of 96.6% was achieved when classifying fruit into three maturity categories (under-ripe, ripe, and over-ripe), and the error was 3.4%. The combination of the optoelectronic device and ANN improves considerably this fruit color classification accuracy, can facilitate the determination of the optimal time for consumption, and optimize the postharvest process efficiency.

Distance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of $m\times m$ pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.

Universal path planning is a major issue in order to facilitate the task required for autonomous systems. Distance transform (wavefront) algorithm is a good path planner and can be used to determine a path near to optimum. We present a new algorithm for universal path planning in exact cell decomposition, using a raster scan method for generating an exact euclidean distance transform (EEDT). This algorithm also find, for every cell in the map, the point sequence to the goal (path smoothing), without post processing. These point sequences are computing by visibility verification between cells.