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The most recent studies show that different methodologies have been adopted to understand the concept of Exergy in general and exergy efficiency, exergy destruction, exergy losses, etc. in particular. The present paper reviews the concept of evacuated tube solar water heating systems (SWHSs) followed by fundamental laws of thermodynamics necessary to provide the concept of exergy analysis in detail. Mathematical modelling and experimental data provide the effect of mass flow rate, temperature gradient, inlet temperature, outlet temperature, collector efficiency, etc. on exergy. Finally, the exergy analysis and exergetic efficiencies along with exergy destruction sources for the evacuated tube collectors are presented.

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The present experimentation work discloses drying of hygroscopic crops under the new concept of solar-assisted greenhouse type dryer integrated with evacuated tube water heating system to control and maintain the temperature of the greenhouse environment according to the regulated flowrate of heated water in the drying trays. The dryer consists of an evacuated tube solar collector, flow regulating device and drying bed with provision for the flow of heated water. The power supply for forced circulation of solar-heated water inside the copper tube as well as the greenhouse environment air is maintained by solar photovoltaic (PV) modules. The dryer is tested for drying two hygroscopic crops namely coriander and fenugreek. The drying performance of the hybrid system is evaluated in terms of mass reduction and its derived influence on moisture content and drying rate. The derived parameters are compared with the corresponding evaluations under open sun drying. The rise in the greenhouse environment temperature and the crop surface temperature at hourly intervals as compared to the ambient condition were used as parameters for the thermal performance of the dryer. The drying curve for change in mass shows complete drying time for coriander and fenugreek reduced by 3.5 and 2.5 h, respectively, for present sample sizes. The most suitable mathematical model was also regressed using matlab followed by the development of a neural network for more precise prediction of moisture ratio (MR) for present hybrid drying.

Pansharpening produces a high spatial‐spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi‐resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA‐based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye‐1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.