Recent Trends in the Early Detection of the Invasive Red Palm Weevil, <em>Rhynchophorus ferrugineus</em> (Olivier)

Red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), is one of the most invasive pest species that poses a serious threat to date palm and coconut palm cultivation as well as the ornamental Canary Island palm. RPW causes massive economic losses in the date palm production sector worldwide. The most important challenge of RPW detection in the early stages of an infestation is the presence of a few externally visible signs. Infested palm shows visible signs when the infestation is more advanced; in this case, the rescuing of infested palms is more complicated. Early detection is a useful tool to eradicate and control RPW successfully. Until now, the early detection techniques of RPW rely mainly on visual inspection and pheromone trapping. Several methods to detect RPW infestation have recently emerged. These include remote sensing, highly sensitive microphones, thermal sensors, drones, acoustic sensors, and sniffer dogs. The main objective of this chapter is to provide an overview of the modern methods for early detection of the RPW and discuss the most important RPW detection technologies that are field applicable

Fiber Optic Sensors and Fiber Lasers

The optical fiber industry is emerging from the market for selling simple accessories using optical fiber to the new optical-IT convergence sensor market combined with high value-added smart industries such as the bio industry. Among them, fiber optic sensors and fiber lasers are growing faster and more accurately by utilizing fiber optics in various fields such as shipbuilding, construction, energy, military, railway, security, and medical.This Special Issue aims to present novel and innovative applications of sensors and devices based on fiber optic sensors and fiber lasers, and covers a wide range of applications of optical sensors. In this Special Issue, original research articles, as well as reviews, have been published

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

Fiber optic sensors for industry and military applications

Fiber optic sensors (FOSs) have been widely used for measuring various physical and chemical measurands owing to their unique advantages over traditional sensors such as small size, high resolution, distributed sensing capabilities, and immunity to electromagnetic interference. This dissertation focuses on the development of robust FOSs with ultrahigh sensitivity and their applications in industry and military areas. Firstly, novel fiber-optic extrinsic Fabry-Perot interferometer (EFPI) inclinometers for one- and two-dimensional tilt measurements with 20 nrad resolution were demonstrated. Compared to in-line fiber optic inclinometers, an extrinsic sensing motif was used in our prototype inclinometer. The variations in tilt angle of the inclinometer was converted into the cavity length changes of the EFPI which can be accurately measured with high resolution. The developed fiber optic inclinometers showed high resolution and great temperature stability in both experiments and practical applications. Secondly, a smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The combination of the transient impact data from FBG and the analyses using machine-learning model provides accurate predictions of the magnitudes, the directions and the types of the impact events. The use of the developed smart helmet system can serve as an early-stage intervention strategy for mitigating and managing traumatic brain injuries within the Golden Hour --Abstract, page iv

Limits of performance of chirped- pulse phase-sensitive OTDR

Distributed acoustic sensing is an emerging field of research which aims to develop methods capable of using a single optical fiber as a long, dense, and high-sensitivity sensor array. Currently, the most promising implementations measure the interference of Rayleigh backscattered light, obtained by probing the fiber with light from a source of high coherence. These methods are known as Phase-sensitive Optical Time-Domain Reflectometers (φOTDR), and are currently undergoing a period of active research and development, both academically and industrially. One of its variants, known as the Chirped-Pulse φOTDR (CP-φOTDR), was developed in 2016. This technique has proven to be remarkably sensitive to strain and temperature, with an attractively simple implementation. In this thesis, we delve into the intricacies of this technique, probing its fundamental limits and addressing current limitations. We discuss the implications of estimation on the performance statistics, the impact of different noise sources and the origin of cross-talk between independent measured positions. In doing so, we also propose methods to reach the current fundamental limitations, and overcome the upper bound of measurable perturbations. We then demonstrate new potential applications of the technique: in seismology, by exploiting the high spatial density of measurements for array signal processing; in the fast characterization of linear birefringence in standard single-mode fibers; and on the measurement of sound pressure waves, by using a special flat cable structure to embed the fiber under test. Finally, we summarize and comment on the aforementioned achievements, proposing some open lines of research that may originate from these results.Distributed acoustic sensing is an emerging field of research which aims to develop methods capable of using a single optical fiber as a long, dense, and highsensitivity sensor array. Currently, the most promising implementations measure the interference of Rayleigh backscattered light, obtained by probing the fiber with light from a source of high coherence. These methods are known as Phase-sensitive Optical Time-Domain Reflectometers (φOTDR), and are currently undergoing a period of active research and development, both academically and industrially. One of its variants, known as the Chirped- Pulse φOTDR (CP-φOTDR), was developed in 2016. This technique has proven to be remarkably sensitive to strain and temperature, with an attractively simple implementation. In this thesis, we delve into the intricacies of this technique, probing its fundamental limits and addressing current limitations. We discuss the implications of estimation on the performance statistics, the impact of different noise sources and the origin of cross-talk between independent measured positions. In doing so, we also propose methods to reach the current fundamental limitations, and overcome the upper bound of measurable perturbations. We then demonstrate new potential applications of the technique: in seismology, by exploiting the high spatial density of measurements for array signal processing; in the fast characterization of linear birefringence in standard single-mode fibers; and on the measurement of sound pressure waves, by using a special flat cable structure to embed the fiber under test. Finally, we summarize and comment on the aforementioned achievements, proposing some open lines of research that may originate from these results

Vibro-Acoustic Codling Moth Larvae Infestation Detection in Apples

Within recent years, the demand for organic produce has greatly increased due to many factors, including increasing knowledge about such things as dietary fiber and balanced gastrointestinal bacterial ecosystems. This increase in demand, coupled with the financial penalties for sending invasive species and pests across borders, presents a need for a scalable and accurate system to non-destructively detect infestation. The proposed work addresses this problem by testing the performance of a non-destructive vibro-acoustic method for detecting lava activity in apples. This involved 3 steps; design a mechanical data collection prototype for testing apples, a evaluate a set of features, and test the detection performance using machine learning algorithms. The mechanical data collection prototype aims to solve some of the issues that arose when collecting repeatable vibro-acoustic data from apples. The second piece aims to show the feasibility of a scalable model which takes vibro-acoustic data, performs multi-domain feature extraction, and then utilizes a SVM/ANN backend to detect codling moth infestation in apples. The final piece describes a procedure in which a novel CNN architecture pair is created to assess the quality of results with and without an acoustic reference channel. The data collection prototype produced higher quality data than previous setups. The feature extraction and SVM/ANN showed the ability to characterize patterns and detect infestation. The best of these was an SVM which had 87.34% accuracy on classifying 5 second segments from apples not in the training set, which was run on one iteration of a randomized dataset split. The CNN architectures showed potential for further development, with the noise-inclusive model performing over 8% better. However, both models show limited potential for generalizing to new apples with accuracies of (35.15% without noise, 43.92% with noise). The lower detection rates were limited by the intermittent larval activity rates, since the low accuracy rates were driven primarily by missed detections in the 5 second windows on apples labeled as infested. If the percentage of activity in any five second window is too low, then the “infested” sample will get classified as healthy due to that window having no larval sounds. The other notable issue regarding generalization potential was the sample size: the number of distinct apples used was too small, especially for deep learning applications. A much larger number of apples will be needed for future work

Sustainable control of infestations using image processing and modelling

A sustainable pest control system integrates automated pest detection and recognition to evaluate the pest density using image samples taken from habitats. Novel predator/prey modelling algorithms assess control requirements for the UAV system, which is designed to deliver measured quantities of naturally beneficial predators to combat pest infestations within economically acceptable timeframes. The integrated system will reduce the damaging effect of pests in an infested habitat to an economically acceptable level without the use of chemical pesticides. Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. The research utilises a combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant and distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for the different datasets. The correspondence filter can achieve rotationally invariant recognition of pests for a full 360 degrees, which proves the effectiveness of the algorithm and provides a count of the number of pests in the image. A series of models has been produced that will permit an assessment of common pest infestation problems and estimate the number of predators that are required to control the problem within a time schedule. A UAV predator deployment system has been designed. The system is offered as a replacement for chemical pesticides to improve peoples’ health opportunities and the quality of food products

Advances in Postharvest Process Systems

This Special Issue presents a range of recent technologies and innovations to help the agricultural and food industry to manage and minimize postharvest losses, enhance reliability and sustainability, and generate high-quality products that are both healthy and appealing to consumers. It focuses on three main topics of food storage and preservation technologies, food processing technologies, and the applications of advanced mathematical modelling and computer simulations. This presentation of the latest research and information is particularly useful for people who are working in or associated with the fields of agriculture, the agri-food chain and technology development and promotion