Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

Urban farming with rooftop greenhouses: a systematic literature review

The environmental impacts of food systems will increase in tandem with rapid urban population growth, which calls for alternative solutions, such as urban agriculture, to reach the United Nations Sustainable Development Goals. Among several urban agriculture systems, rooftop farming and its subset, rooftop greenhouses, are promising technologies. They optimize land use, increase profitability for building owners, deliver good yields per unit area, increase water use efficiency, and reduce the energy use of both greenhouse and host buildings while mitigating the urban heat island effect. A systematic literature review of the rooftop greenhouse technology was carried out to examine the benefits and challenges associated with this technology. This review was based on 45 articles, covering themes such as the impact of rooftop greenhouse technology on yields, energy use, water use, environmental impacts, and life-cycle costs; some benefits identified are the symbiotic heat, water, and CO2 exchanges between the rooftop greenhouse and its host building, and the possibility of delivering yearround production. The additional investment, operational costs, limited availability of flat roofs, and various regulations were challenges to overcome. The relevance of symbiosis between rooftop greenhouses and buildings to enhancing sustainability, and meeting the SDGs was explored. This review also outlines that rooftop greenhouses are increasing in scale, system diversity, societal acceptance and popularity among commercial operations in large cities. The future of rooftop farming lies in customizing the right technology for selected building typologies globally, where food production is fully integrated into the urban landscape

Inflation forecasting by hybrid singular spectrum analysis – multilayer perceptrons neural network method, case of Indonesia

Inflation is one of the most important macroeconomic indicators which affects the economic condition of a nation. Therefore, it is necessary to maintain its stability in order that it will not lead to a negative impact and an economic vulnerability. The drastic change in the rate of inflation is determined by the condition of the price of goods which is affected by the distribution and supply-demand factors of goods. As a consequence, it becomes a very important act of action to control inflation. This can be achieved by meeting the information needs of future inflation rates that is needed for the government and the policy of the monetary authority. Fulfillment of accurate and reliable future forecasts of future inflation estimates can be obtained through forecasting. This paper examines the application of the method of Hybrid singular spectrum analysis - a multilayer perceptions neural network to predict the inflation. The main data source used is monthly inflation (in percent) collected by BPS Statistics Indonesia. The result of the study found that the ability of SSA-MPNN Hybrid method is good enough in predicting monthly inflation, as it is provided by the MAPE value of 35.42 percent, without-sample of three observations

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications

Introduction and adoption of innovations in horticultural production systems

Horticultural production occurs in various production systems, dominated by greenhouse and open-field production. During the last decade, alternative production systems with more advanced technologies, such as LED lighting and artificial intelligence, have started to appear, e.g., plant factories with artificial lighting. This opens up new opportunities where increased attention from venture capitalists and investors highlights food-tech as an innovative field of interest. Technological development can also accelerate possibilities, mainly for firms producing in greenhouses, if they can adopt relevant knowledge and innovations from other production systems. Another aspect is the increased interest in start-up initiatives and businesses in urban settings, e.g., urban farming, vertical farming, aquaponics, or rooftop greenhouses, to mention a few models. In parallel, low-tech initiatives are developing, e.g., market gardening and small-scale artisan production, which can also be important niches for the sustainable production of vegetables. The innovative production systems often use alternative food networks and different business models, e.g., Community Supported Agriculture or Product Service Systems, often with shorter supply chains. These different initiatives are also associated with positive movements influencing society and increasing consumers’ awareness of sustainable food production. However, the fact that new actors are entering the market could also create tensions between urban and rural contexts due to the different backgrounds of business owners. This is further accelerated by the different conditions for the firms, e.g., depending on support and policies from the innovation system and society in general

Author-Topic Modeling of DESIDOC Journal of Library and Information Technology (2008-2017), India

This study presents a method to analyze textual data and applying it to the field of Library and Information Science. This paper subsumes a special case of Latent Dirichlet Allocation and Author-Topic models where each article has one unique author and each author has one unique topic. Topic Modeling Toolkit is used to perform the author-topic modeling. The study further which considers topics and their changes over time by taking into account both the word co-occurrence pattern and time. 393 full-text articles were downloaded from DESIDOC Journal of Library and Information Technology and were analyzed accordingly. 16 core topics have been identified throughout the period of ten years. These core topics can be considered as the core area of research in the journal from 2008 to 2017. This paper further identifies top five authors associated with the representative articles for each studied year. These authors can be treated as the subject-experts for the modeled topics as indicated. The results of the study can serve as a platform to determine the research trend; core areas of research; and the subject-experts related to those core areas in the field the Library and Information Science in India

Digital twin for civil engineering systems: an exploratory review for distributed sensing updating

We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.This research was funded by Fondazione CARITRO Cassa di Risparmio di Trento e Rovereto, grant number 2021.0224.Peer ReviewedPostprint (published version