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

Pictures in Your Mind: Using Interactive Gesture-Controlled Reliefs to Explore Art

Tactile reliefs offer many benefits over the more classic raised line drawings or tactile diagrams, as depth, 3D shape, and surface textures are directly perceivable. Although often created for blind and visually impaired (BVI) people, a wider range of people may benefit from such multimodal material. However, some reliefs are still difficult to understand without proper guidance or accompanying verbal descriptions, hindering autonomous exploration. In this work, we present a gesture-controlled interactive audio guide (IAG) based on recent low-cost depth cameras that can be operated directly with the hands on relief surfaces during tactile exploration. The interactively explorable, location-dependent verbal and captioned descriptions promise rapid tactile accessibility to 2.5D spatial information in a home or education setting, to online resources, or as a kiosk installation at public places. We present a working prototype, discuss design decisions, and present the results of two evaluation studies: the first with 13 BVI test users and the second follow-up study with 14 test users across a wide range of people with differences and difficulties associated with perception, memory, cognition, and communication. The participant-led research method of this latter study prompted new, significant and innovative developments

A model-driven approach to broaden the detection of software performance antipatterns at runtime

Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that predicate on four views: (i) the static view that captures the software elements (e.g. classes, components) and the static relationships among them; (ii) the dynamic view that represents the interaction (e.g. messages) that occurs between the software entities elements to provide the system functionalities; (iii) the deployment view that describes the hardware elements (e.g. processing nodes) and the mapping of the software entities onto the hardware platform; (iv) the performance view that collects specific performance indices. In this paper we present a lightweight infrastructure that is able to detect performance antipatterns at runtime through monitoring. The proposed approach precalculates such predicates and identifies antipatterns whose static, dynamic and deployment sub-predicates are validated by the current system configuration and brings at runtime the verification of performance sub-predicates. The proposed infrastructure leverages model-driven techniques to generate probes for monitoring the performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043

A survey of image processing techniques for agriculture

Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts

GTTC Future of Ground Testing Meta-Analysis of 20 Documents

National research, development, test, and evaluation ground testing capabilities in the United States are at risk. There is a lack of vision and consensus on what is and will be needed, contributing to a significant threat that ground test capabilities may not be able to meet the national security and industrial needs of the future. To support future decisions, the AIAA Ground Testing Technical Committees (GTTC) Future of Ground Test (FoGT) Working Group selected and reviewed 20 seminal documents related to the application and direction of ground testing. Each document was reviewed, with the content main points collected and organized into sections in the form of a gap analysis current state, future state, major challenges/gaps, and recommendations. This paper includes key findings and selected commentary by an editing team

Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars

Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.info:eu-repo/semantics/acceptedVersio

Detection of dish manufacturing defects using a deep learning-based approach

Quality control is essential to ensure the smooth running of an industrial process. This work proposes to use and adapt a deep learning-based algorithm that will integrate an automatic quality control system at a porcelain dish factory. This system will receive images acquired in real time by high resolution cameras directly placed on production line. The algorithm proposed in this research work will classify the dishes presented in the images as "defective" or "without defect". Therefore, the objective of the system will be the detection of defective dishes, causing fewer defective dishes to reach the market, thus contributing to a better reputation of the factory. This system is based on the application of an algorithm called Convolutional Neural Network. This algorithm requires a large amount of data to be trained and to perform the image classification. Since the COVID-19 pandemic was felt on a larger scale in Portugal at the time of the development of this research work, it was impossible to obtain data directly from the factory. Due to this setback, the data used in this work was artificially generated. By providing the complete images of dishes to the algorithm, it achieved a defect detection accuracy of 92.7% with the first dataset and 91.9%. with the second. When providing the algorithm 100x100 pixel segments of the original images, using the second created dataset, it reached 91.6% accuracy in the classification of these segments, which translated into a 52.0% accuracy rate in the classification of the complete dish images.O controlo de qualidade é fundamental para assegurar o bom funcionamento de um processo industrial. Este trabalho propõe a utilização e adaptação de um algoritmo, baseado em aprendizagem profunda, como parte integrante de um sistema automático de controlo de qualidade numa fábrica de pratos de porcelana. Este sistema receberá imagens adquiridas em tempo real por câmaras fotográficas colocadas diretamente sobre a linha de produção. O algoritmo utilizado classificará os pratos presentes nas imagens como "defeituoso" ou "sem defeito". O objetivo do sistema será, portanto, a deteção de pratos defeituosos, fazendo com que menos pratos com defeito cheguem ao mercado, contribuindo assim para uma melhor reputação da fábrica. Este sistema é baseado na aplicação de uma rede neuronal convolucional. Este tipo de redes requer um elevado número de dados para ser treinado de modo a conseguir realizar a classificação de imagens. Uma vez que a pandemia de COVID-19 se fez sentir em maior escala em Portugal na altura do desenvolvimento deste trabalho, foi impossível a obtenção de imagens provenientes da fábrica. Devido a este contratempo, os dados utilizados neste trabalho foram gerados artificialmente. Ao fornecer imagens completas de pratos ao algoritmo, o mesmo atingiu uma taxa de acerto da deteção de defeitos de 92,7% com o primeiro conjunto de dados e 91,9% com o segundo. Ao fornecer ao algoritmo segmentos de 100x100 pixéis da imagem original, o mesmo atingiu 91,6% de taxa de acerto, o que se traduziu numa taxa de acerto de 52,0% na classificação das imagens completas de pratos

Deteção automática de defeitos em couro

Dissertação de mestrado em Informatics EngineeringEsta dissertação desenvolve-se em torno do problema da deteção de defeitos em couro. A deteção de defeitos em couro é um problema tradicionalmente resolvido manualmente, usando avaliadores ex perientes na inspeção do couro. No entanto, como esta tarefa é lenta e suscetível ao erro humano, ao longo dos últimos 20 anos tem-se procurado soluções que automatizem a tarefa. Assim, surgiram várias soluções capazes de resolver o problema eficazmente utilizando técnicas de Machine Learning e Visão por Computador. No entanto, todas elas requerem um conjunto de dados de grande dimensão anotado e balanceado entre as várias categorias. Assim, esta dissertação pretende automatizar o processo tradicio nal, usando técnicas de Machine Learning, mas sem recorrer a datasets anotados de grandes dimensões. Para tal, são exploradas técnicas de Novelty Detection, as quais permitem resolver a tarefa de inspeção de defeitos utilizando um conjunto de dados não supervsionado, pequeno e não balanceado. Nesta dis sertação foram analisadas e testadas as seguintes técnicas de novelty detection: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. Estas técnicas foram treinadas e testadas com dois conjuntos de dados diferentes: MVTEC e Neadvance. As técnicas analisadas detectam e localizam a mai oria dos defeitos das imagens do MVTEC. Contudo, têm dificuldades em detetar os defeitos das imagens do dataset da Neadvance. Com base nos resultados obtidos, é proposta a melhor metodologia a usar para três diferentes cenários. No caso do poder computacional ser baixo, SSIM Autoencoder deve ser a técnica usada. No caso onde há poder computational suficiente e os exemplos a analisar são de uma só cor, DRAEM deve ser a técnica escolhida. Em qualquer outro caso, o STFPM deve ser a opção escolhida.This dissertation develops around the leather defects detection problem. The leather defects detec tion problem is traditionally manually solved, using experient assorters in the leather inspection. However, as this task is slow and prone to human error, over the last 20 years the searching for solutions that automatize this task has continued. In this way, several solutions capable to solve the problem effi ciently emerged using Machine Learning and Computer Vision techniques. Nonetheless, they all require a high-dimension dataset labeled and balanced between all categories. Thus, this dissertation pretends to automatize the traditional process, using the Machine Learning techniques without requiring a large dimensions labelled dataset. To this end, there will be explored Novelty Detection techniques, that in tend to solve the leather inspection task using an unsupervised small and non-balanced dataset. This dissertation analyzed and tested the following Novelty Detection techniques: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. These techniques are trained and tested in two distinct datasets: MVTEC and Neadvance. The analyzed techniques detect and localize most MVTEC defects. However, they have difficulties in defect detection on Neadvance samples. Based on the ob tained results, it is proposed the best methodology to use for three distinct scenarios. In the case where the computational power available is low, SSIM Autoencoder should be the technique to use. In the case where there is enough computational power and the samples to inspect have the same color, DRAEM should be the chosen technique. In any other case, the STFPM should be the chosen option