Shanmuga Sundaram Anandan

Recent advancements in imaging, electronics, and computer science have engendered significant progress in non-destructive testing and quality monitoring within the agro-food industry. This progress is particularly evident in integrating infrared thermal imaging (TI) and artificial intelligence (AI) techniques. As a non-contact method, AI-based TI holds promise in detecting various quality attributes and has found extensive applications in agriculture, food processing, and post-harvest fruit handling. This paper delves into recent applications of AI-based thermal imaging, specifically in post-harvest fruit handling. The introduction provides a comprehensive overview of the challenges faced in the post-harvest fruit handling industry while emphasizing the advantages of AI-driven thermal imaging technology. The detailed thermal imaging system encompasses both passive and active thermography techniques. This paper provides an in-depth exploration of artificial intelligence, focusing on machine learning and deep learning. It highlights the significance of convolutional neural networks (CNNs) and their architectural phases. Subsequently, critical applications of AI-based thermal imaging in post-harvest fruit quality assessment are discussed. These applications encompass bruise detection, maturity identification, condition monitoring, grading and sorting, pest and disease detection, and considerations for packaging and supply chain management. Furthermore, this paper addresses the challenges and limitations of AI-based thermal imaging in post-harvest fruit handling. In conclusion, this paper discusses future trends in AI-based thermal imaging, emphasizing the potential for increased automation and integration with emerging technologies in the post-harvest fruit handling sector. The insights provided contribute to the ongoing dialog surrounding optimizing quality assessment processes in the agro-food industry.

Thermal imaging has the potential to measure the object’s surface temperature. This study investigated the thermal behavior of mango fruit stored in a refrigerated environment. Thermal images of the fruit were collected with sufficient quality by supplying hot air to the acquisition environment. Grey-Level Co-occurrence Matrix (GLCM) features of mango images were determined to distinguish the subtle and noticeable changes. The thermal images were analyzed to find the temperature difference between the different regions of the fruit. The temperature of the bruise boundary ( T _bd ) was higher than the bruised center ( T _C ) throughout the storage period. In addition, an enhanced deep-learning model was used to predict the damaged mango. Over 10 days, 3500 thermal images were obtained from the 400 mangoes. In that, 80 % of the images were used for training, 10 % for testing, and 10 % for validation. The model achieved a classification accuracy of 99.6 %.

Guava is a fruit grown predominantly in the tropical and subtropical regions of the world. Its skin is thin and soft. Although postharvest handling and transportation of fruits are unavoidable, hand harvesting is preferred to avoid physical injuries. Visually identifying internal damage is a laborious and time-consuming task. In this study, 4129 thermal and digital images of guava fruits were collected. A digital image-based convolution neural network (CNN) model was developed to classify the quality of damaged and diseased fruits. The surface temperatures of all three levels of maturity-indexed and damaged fruits were measured using thermal images. During the storage period, the temperature of the immature fruits was less than half of the mature fruits. Because the architecture was trained on dehydrated and diseased guava, it accurately predicted fruit quality. The prediction accuracy of the developed CNN model was approximately 99.92%. GRAPHICAL ABSTRACT

Industrial applications consume about one–third of global demand and account for nearly fifty percent of unutilized waste heat. Industrial waste heat recovery is a suitable solution. The tapping of waste heat from industrial activities has become inevitable energy conservation technology to reduce energy consumption and minimize the usage of fossil fuels to reduce carbon dioxide emissions. The conventional waste heat recovery installed on-site to meet local energy demand is a well-established technology. However, the topological mismatch between energy recovery and its demand remains a hurdle, especially for off-site heat demand located far away from sources. The Mobilized Thermal Energy Storage (MTES) alligates this issue. The economic and environmental study of MTES revealed that the standard energy cost (€/MWh) is proportional to transport distance. The energy cost (€/MWh) ranges from 40 to 80 with latent heat storage capacities latent heat storage capacity between 1.4 and 2.5 MWh and transport distance of 2 to 50 Km between source and end-users. The use of MTES reduces carbon emission up to 90% in comparison with conventional heating. Therefore, the transportation of waste utilizing thermal energy storage has become a trusted area of research.

In this study, the investigation on thermal property and characteristic analysis of Poly Ethylene Glycol (PEGs) material by varying the molecular weights is examined. In this context, the study on thermal energy material storage is explored by using the different techniques such as Differential Scanning Calorimetry (DSC) for persistent heating rate and Thermal Gravity Analysis (TGA) for determining the amount of change of mass of the material in addition to the function of increasing temperature. Subsequently, the characterization of PEGs with different molecular weight is carried out to explore the property and insight of the material by using the Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) by examining the surface topology, crystallography in addition to this the X-Ray Diffraction (XRD) techniques for catch a glimpse of the crystal structure is studied. As an outcome, in the DSC techniques the results made known when the molecular weight increases, correspondingly the melting temperature and solidification temperature similarly increases however, the heat of fusion during the freezing cycle increases. In the TGA techniques, similarly specified increases in the volume of PEG with different molecular weight has resulted in an increase in the temperature. Subsequently, in the characterization of the PEGs, the SEM has shown an enormous amount of insignificant sphere-shaped crystal-like structures which are joined together and impinging on their neighbour’s which paved the way for the formation of a multi-layered lamellar structure in due course activated rise in the solidification temperature in its molecular weight. Henceforth, from the analytical studies, it’s been evident that the molecular weight greatly influences the mechanical and thermal properties of PEGs. In this concern, the PEGs blends are a potential benefit to replace pure components.

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Thermo-economic analysis of Heat Pipe Heat Exchanger (HPHE) for waste heat recovery in thermal systems using the P1-P2 and NTU methods is presented. The optimum effectiveness of a HPHE is given as a polynomial equation, whereas the net savings and payback period are given in closed form to enable the HPHE designer to determine the optimised values directly. In addition to this, exergy analysis of HPHE for waste heat recovery in thermal systems is formulated. The variation of exergy efficiency with respect to change in inlet temperature is also presented.

In India, the energy consumption in the building sector is rapidly increasing due to improvement in living standards. Effective thermal insulation of building walls is one of the most effective energy conservation measures for heating, ventilation, and air conditioning applications in buildings. In this study, the thermoeconomic optimisation of insulation thickness on walls of buildings is analysed based on degree days. Thermoeconomic parameters such as optimum insulation thickness, annual electrical energy consumption, annual energy cost and payback period is determined for three different insulation materials for the cities located in India. Database on insulation materials for five cities of India are provided.