Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy the different requirements of the inspection: simultaneously detect transmission towers, check for defects, and analyze security distances. Following this direction, this paper proposes a supervised learning approach for solving the tower detection and classification problem, where HOG (Histograms of Oriented Gradients) features are used to train two MLP (multi-layer perceptron) neural networks. The first classifier is used for background-foreground segmentation, and the second multi-class MLP is used for classifying within 4 different types of electric towers. A thorough evaluation of the tower detection and classification approach has been carried out on image data from real inspections tasks with different types of towers and backgrounds. In the different evaluations, highly encouraging results were obtained. This shows that a learning-based approach is a promising technique for power line inspection.

Purpose of Review: This article explores the current state of robotic technology and its potential to improve post-harvest supply chain quality management and decision-making. Recent Findings: Soft-robotics is paving the way towards generalized gripping solutions, which are highly relevant for handling fragile fresh products. Besides gripping potential, it is imperative to study the impact of these solutions on fresh product quality and safety. Automated mobile robots, AMRs, present new opportunities for automating post-harvest warehouse operations. Moreover, AMRs enable new possibilities to monitor and map qualitative and climatic changes, and to take quality-driven (semi-)autonomous actions. Summary: A review of advances in robotic manipulation and navigation technologies is presented in terms of their potential to monitor, maintain, and preserve the fresh produce quality. Soft-robotics and AMRs offer technologies with the potential to transform post-harvest supply chains. Future research should focus on leveraging these technologies and investigate their potential for quality management and quality-driven decision-making.

Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010–2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors.

This paper addresses word learning for human–robot interaction. The focus is on making a robotic agent aware of its surroundings, by having it learn the names of the objects it can find. The human user, acting as instructor, can help the robotic agent ground the words used to refer to those objects. A lifelong learning system, based on one-class learning, was developed (OCLL). This system is incremental and evolves with the presentation of any new word, which acts as a class to the robot, relying on instructor feedback. A novel experimental evaluation methodology, that takes into account the open-ended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. The results indicate that the robot’s representations are capable of incrementally evolving by correcting class descriptions, based on instructor feedback to classification results. In successive experiments, it was possible for the robot to learn between 6 and 12 names of real-world office objects. Although these results are comparable to those obtained by other authors, there is a need to scale-up. The limitations of the method are discussed and potential directions for improvement are pointed out.

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000–1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390–1420 nm contributes most to the model’s final decision.