JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

Surface Defect Detection (SDD) is a significant research content in Industry 4.0 field. In the real complex industrial environment, SDD is often faced with many challenges, such as small difference between defect imaging and background, low contrast, large variation of defect scale and diverse types, and large amount of noise in defect images. Jujubes are naturally growing plants, and the appearance of the same type of surface defect can vary greatly, so it is more difficult than industrial products produced according to the prescribed process. In this paper, a ConvNeXt-based high-precision lightweight classification network JujubeNet is presented to address the practical needs of Jujube Surface Defect (JSD) classification. In the proposed method, a Multi-branching module using Depthwise separable Convolution (MDC) is designed to extract more feature information through multi-branching and substantially reduces the number of parameters in the model by using depthwise separable convolutions. What’s more, in our proposed method, the Convolutional Block Attention Module (CBAM) is introduced to make the model concentrate on different classes of JSD features. The proposed JujubeNet is compared with other mainstream networks in the actual production environment. The experimental results show that the proposed JujubeNet can achieve 99.1% classification accuracy, which is significantly better than the current mainstream classification models. The FLOPS and parameters are only 30.7% and 30.6% of ConvNeXt-Tiny respectively, indicating that the model can quickly and effectively classify JSD and is of great practical value

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task

Line-based deep learning method for tree branch detection from digital images

The final publication is available at Elsevier via https://doi.org/10.1016/j.jag.2022.102759. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 licensePreventive maintenance of power lines, including cutting and pruning of tree branches, is essential to avoid interruptions in the energy supply. Automatic methods can support this risky task and also reduce time consuming. Here, we propose a method in which the orientation and the grasping positions of tree branches are estimated. The proposed method firstly predicts the straight line (representing the tree branch extension) based on a convolutional neural network (CNN). Secondly, a Hough transform is applied to estimate the direction and position of the line. Finally, we estimate the grip point as the pixel point with the highest probability of belonging to the line. We generated a dataset based on internet searches and annotated 1868 images considering challenging scenarios with different tree branch shapes, capture devices, and environmental conditions. Ten-fold cross-validation was adopted, considering 90% for training and 10% for testing. We also assessed the method under corruptions (gaussian and shot) with different severity levels. The experimental analysis showed the effectiveness of the proposed method reporting F1-score of 96.78%. Our method outperformed state-of-the-art Deep Hough Transform (DHT) and Fully Convolutional Line Parsing (F-Clip).This research was funded by CNPq (p: 433783/2018–4, 310517/2020–6, 314902/2018–0, 304052/2019–1 and 303559/2019–5), FUNDECT (p: 59/300. 066/2015, 071/2015) and CAPES PrInt (p: 88881.311850/2018–01). The authors acknowledge the support of the UFMS (Federal University of Mato Grosso do Sul) and CAPES (Finance Code 001). This research was also partially supported by the Emerging Interdisciplinary Project of Central University of Finance and Economics

Pro-poor adaptation to climate variability in semi-arid regions of Zimbabwe: the role of Ziziphus Mauritiana and network interventions in Muzarabani district

A Thesis submitted to the Faculty Of Science, at the University Of The Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor Of Philosophy in Geography and Environmental StudiesThe study seeks to interrogate the role that Ziziphus mauritiana plays, as a non- human actor, in building the adaptive and resilience capacity of poor people in Muzarabani of Zimbabwe through the lens of the Actor Network Theory (ANT) and Commodity Chain Analysis (CCA). The qualitative approach was largely used. Empirical evidence, which is largely the role that Zm plays in the adaptive capacity and resilience discourses on semi-arid space of Muzarabania as established by observations, interviews with diverse actors that included household heads, traditional leadership, civil society organisation (CSOs)and government department is presented in this thesis. The study revealed that there is a myriad of actors that form a complex web of adaptive and resilience capacity in Zm production, processing and marketing in Muzarabani. The analysis and discussion of the results places Zm into perspective, illustrating how Zm could enhance the adaptive capacity and resilience in Muzarabani if it were given the attention it deserves. Furthermore, the study revealed that local communities are not passive, as often portrayed, when they experience adverse weather conditions. Excluding them from processes that directly influence their day to day experience, for example the use of Zm, defeats all efforts to address environmental challenges. The case of Zm is argued with the lens of ANT. The study goes further “to unpack the black box” using ANT. Ultimately the research proposes the Actor Networked Eco-based Resilience Logic Model (ANELRM) as the framework that can catapult Zm to the right position in the adaptive capacity and resilience discourse in Muzarabani. The study further demonstrated another contribution to ANT that is it considers the natural resource Ziziphus mauritiana (Zm) as a non-linear commodity that needs to be followed closely to fully understand its contribution, contrary to the idea of taking natural resources as linear and static. ANT views power relations, not in terms of the physical power, but in terms of connections, offering a base to suggest it can, to some extent, address social inequality, exclusion and power dynamics which characterise local natural resource use. This is important to policy makers as they would understand better actors and the natural resources influencing community resilience after drought or floods. In context this will help government actors, like the Muzarabani Rural District Council, to come up with contextual policy measures and implementation efforts to address resilience challenges in semi-arid spaces of SSA, specifically in Zimbabwe.XL201

Non-destructive Detection of Food Adulteration to Guarantee Human Health and Safety

The primary objective of this review is to critique the basic concepts of non-destructive detection of food adulteration and fraud which collectively represent a tremendous annual financial loss worldwide and a major cause of human disease. The review covers the principles of the analytical instrumentation used for the non-destructive detection of food adulteration. Examples of practical applications of these methods for the control of food adulteration are provided and a comparative analysis of the advantages and disadvantages of instrumental methods in food technology are critiqued.Целью данного обзора является критическое рассмотрение основных понятий неразрушающего выявления фальсификации и подделки продуктов питания, которые в целом вызывают огромные ежегодные финансовые убытки во всем мире и являются одной из основных причин заболеваний человечества. Материалы и методы. Литература, указанная в данном обзоре, была получена в результате поиска библиографической информации в CAB abstracts, AGRICOLA, SciFinder Scholar, Modern Language Association (MLA), American Psychological Association (APA), OECD / EEA database по инструментам, которые используются для экологической политики и управления природными ресурсами, и Web of Science.Результаты и обсуждение. Фальсификация пищевых продуктов означает преднамеренное, обманное добавление посторонних, нестандартных или дешевых ингредиентов в продукты, или разбавление или удаление некоторых ценных ингредиентов с целью увеличения прибыли. В современных условиях производители стремятся увеличить выпуск своей продукции зачастую путем изготовления и продажи некачественных и фальсифицированных продуктов.“Неразрушающее выявление фальсификации пищевых продуктов” означает анализ образца и его существенных признаков без изменения физических и химических свойств образца. Повышение качества и безопасности пищевых продуктов путем разработки научных методов обнаружения фальсификации является главным условием для поддержания здоровья потребителей. Точная объективная оценка качества и выявление фальсификации пищевых продуктов представляется важнейшей целью пищевой промышленности. В связи с совершенствованием технологии фальсификации продуктов важно быть в курсе современных, самых точных методов контроля их фальсификации. С этой целью данный обзор рассматривает основные понятия выявления фальсификации продуктов питания, принципы устройств и возможные практические применения современных методов неразрушающего выявления фальсификации продуктов питания; сравнительный анализ преимуществ и недостатков инструментальных методов, используемых в пищевых технологиях. Каждый из рассмотренных методов обсуждается с точки зрения возможных различных консистенций продуктов – газов (свободного пространства вокруг продукта), свободно текущих жидкостей (соков), мутных и вязких жидкостей (меда как продукта растительного происхождения, растительных масел) и интактных продуктов (фруктов и овощей).Выводы. Результаты, освещенные в обзоре, рекомендуется использовать при контроле качества и безопасности пищевых продуктов.Метою даного огляду є критичний розгляд основних понять неруйнівного виявлення фальсифікації і підробки продуктів харчування, які в цілому викликають величезні щорічні фінансові збитки у всьому світі і є однією з основних причин захворювань людства. Матеріали і методи. Література, зазначена в даному огляді, була отримана в результаті пошуку бібліографічної інформації в in CAB abstracts, AGRICOLA, SciFinder Scholar, Modern Language Association (MLA), American Psychological Association (APA), OECD/EEA database щодо інструментів, які використовуються для екологічної політики та управління природними ресурсами, та Web of Science. Результати та обговорення. Фальсифікація харчових продуктів означає умисне, облудне додавання сторонніх, нестандартних або дешевих інгредієнтів в продукти, або розбавлення чи видалення деяких цінних інгредієнтів з метою збільшення прибутків. У сучасних умовах виробники прагнуть збільшити випуск своєї продукції найчастіше шляхом виготовлення та продажу неякісних та фальсифікованих продуктів. “Неруйнівне виявлення фальсифікації харчових продуктів” означає аналіз зразка і його істотних ознак без зміни фізичних і хімічних властивостей зразка. Підвищення якості та безпеки харчових продуктів шляхом розробки наукових методів виявлення фальсифікації є головною умовою для підтримки здоров’я споживачів. Точна об’єктивна оцінка якості і виявлення фальсифікації харчових продуктів представляється найважливішою метою харчової промисловості. У зв’язку з удосконаленням технології фальсифікації продуктів важливо бути в курсі сучасних, найбільш точних методів контролю їх фальсифікації. З цією метою даний огляд розглядає основні поняття виявлення фальсифікації продуктів харчування, принципи пристроїв і можливі практичні застосування сучасних методів неруйнівного виявлення фальсифікації продуктів харчування; порівняльний аналіз переваг і недоліків інструментальних методів, що застосовуються в харчових технологіях. Кожен з розглянутих методів обговорюється з точки зору можливих різних консистенцій продуктів - газів (вільного простору навколо продукту), вільно текучих рідин (соків), каламутних та в'язких рідин (меду як продукту рослинного походження, рослинних масел) і інтактних продуктів (фруктів і овочів). Висновки. Результати, висвітлені в огляді, рекомендується використовувати під час контролю якості та безпеки харчових продуктів

Dendrobium candidum quality detection in both food and medicine agricultural product: Policy, status, and prospective

Dendrobium candidum (DC) is an agricultural product for both food and medicine. It has a variety of beneficial effects on the human body with antioxidant, anti-inflammatory, antitumor, enhancing immune function, and other pharmacological activities. Due to less natural distribution, harsh growth conditions, slow growth, low reproduction rate, and excessive logging, wild DC has been seriously damaged and listed as an endangered herbal medicine variety in China. At present, the quality of DC was uneven in the market, so it is very necessary to detect its quality. This article summarized the methods of DC quality detection with traditional and rapid nondestructive, and it also expounded the correlation between DC quality factor and endophytes, which provides a theoretical basis for a variety of rapid detection methods in macromolecules. At last, this article put forward a variety of rapid nondestructive detection methods based on the emission spectrum. In view of the complexity of molecular structure, the quality correlation established by spectral analysis was greatly affected by varieties and environment. We discussed the possibility of DC quality detection based on the molecular dynamic calculation and simulation mechanism. Also, a multimodal fusion method was proposed to detect the quality. The literature review suggests that it is very necessary to understand the structure performance relationship, kinetic properties, and reaction characteristics of chemical substances at the molecular level by means of molecular chemical calculation and simulation, to detect a certain substance more accurately. At the same time, several modes are combined to form complementarity, eliminate ambiguity, and uncertainty and fuse the information of multiple modes to obtain more accurate judgment results

Automatic early detection of decay in citrus fruit using optical technologies and machine learning techniques

Los cítricos representan el cultivo frutal de mayor valor en términos de comercio internacional, siendo España el primer exportador mundial de cítricos para consumo en fresco. Sin embargo, la presencia de podredumbres causadas por hongos del género Penicillium se encuentra entre los principales problemas que afectan la postcosecha y comercialización de cítricos. Un número reducido de frutas infectadas puede contaminar una partida completa de cítricos durante el almacenamiento de la fruta por largos períodos de tiempo o en el transporte al extranjero, lo que conlleva grandes pérdidas económicas y el desprestigio de los productores de cítricos. Por lo tanto, la detección temprana de infecciones por hongos de forma efectiva y la eliminación de la fruta infectada son asuntos de especial interés en los almacenes de confección de fruta para impedir la propagación de las infecciones fúngicas, asegurando de esta forma una excelente calidad de la fruta y la ausencia total de fruta infectada. En este sentido, la presente tesis doctoral se centra en abordar un reto tan importante para la industria citrícola como es la automatización del proceso de detección de podredumbres incipientes, con el fin de proporcionar alternativas a la inspección manual con peligrosa luz ultravioleta que permitan realizar esta detección de forma más eficiente y, en consecuencia, reducir potencialmente el uso de fungicidas. En concreto, esta tesis doctoral avanza en el campo de la detección automática de podredumbres en cítricos mediante sistemas ópticos y técnicas de aprendizaje automático. Específicamente, se investigan tres técnicas ópticas diferentes que operan en las regiones del visible e infrarrojo cercano del espectro electromagnético, incluyendo la técnica de imagen basada en backscattering, visión hiperespectral y espectroscopía. Los sistemas ópticos usados en esta tesis no están limitados a la parte visible del espectro, por lo que sus capacidades superan a las del ojo humano y a las de los sistemas de visión convencionales basados en cámaras de color, lo cual resulta de especial interés para detectar daños en cítricos que son difícilmente visibles a simple vista, como las podredumbres en estadios tempranos de infección. Además, se exploran numerosas técnicas de aprendizaje automático de reducción de la dimensionalidad de los datos y clasificación, con la finalidad de usar las medidas ópticas de los cítricos para discriminar la fruta afectada por podredumbre de la fruta sana. Las tres técnicas ópticas, junto con métodos de aprendizaje automático adecuados, proporcionan buenos resultados en la clasificación de la piel de los frutos cítricos en sana o podrida, consiguiendo un porcentaje de muestras bien clasificadas superior al 90% para ambas clases, a pesar de la gran similitud entre ellas. En vista de los resultados obtenidos, esta tesis doctoral sienta las bases para la futura implementación de las técnicas ópticas estudiadas en un sistema comercial de clasificación automática de fruta destinado a la detección de podredumbres en cítricos.Citrus fruit is the highest value fruit crop in terms of international trade, with Spain being the first worldwide exporter of citrus fruit for fresh consumption. However, the presence of decay caused by Penicillium spp. fungi is among the main problems affecting postharvest and marketing processes of citrus fruit. A small number of decayed fruit can infect a whole consignment, during long-term storage or fruit shipping to export markets, thus involving enormous economic losses and the blackening of the reputation of citrus producers. Therefore, effective early detection of fungal infections and removal of infected fruit are issues of major concern in commercial packinghouses in order to prevent the spread of the infections, thus ensuring an excellent fruit quality and absolute absence of infected fruit. In this respect, this doctoral thesis focuses on addressing such an important challenge for the citrus industry as the automation of the detection of early symptoms of decay, in order to provide alternatives to human inspection under dangerous ultraviolet illumination, thus accomplishing this detection task more efficiently and, consequently, leading to a possible reduction of the use of fungicides. Specifically, this doctoral thesis advances in the field of the automatic detection of decay in citrus fruit using optical systems and machine learning methods. In particular, three different optical techniques operating in the visible and near-infrared spectral regions are investigated, including hyperspectral imaging, light backscattering imaging and spectroscopy. The optical systems used in this thesis are not limited to the visible part of the electromagnetic spectrum, thus presenting capabilities beyond those of the naked human eye and traditional computer vision systems based on colour cameras, this fact being of special interest for detecting hardly-visible damage in citrus fruit, such as decay at early stages. Furthermore, a vast number of machine learning techniques aimed at data dimensionality reduction and classification are explored for dealing with the optical measurements of citrus fruit in order to discriminate fruit with symptoms of decay from sound fruit. The three optical techniques, coupled with suitable machine learning methods, investigated in this doctoral thesis provide good results in the classification of skin of citrus fruit into sound or decaying, with a percentage of well-classified samples above 90% for both classes despite their similarity. In the light of the results, this doctoral thesis lays the foundation for the future establishment of the explored optical technologies on a commercial fruit sorter aimed at decay detection in citrus fruit