Deep learning has recently been hailed as the most advanced computer vision technology for image classification. The invention of convolutional neural network (CNN) simplified the effort of feature engineering. Classification of various stages of fruit maturity using machine learning algorithms is a difficult task since it is difficult to distinguish the visual features of the fruits at different maturity stages. Fruit ripeness is critical in agriculture since it impacts the quality of the fruit. Manually determining the maturity of the fruit has various flaws, including the fact that it takes a long time, needs a lot of labor, and can lead to inconsistencies. In developing countries, agriculture is one of the most important economic sectors. Created system can be employed in the food processing business, in real-life applications where the intelligent system’s accuracy, cost, and speed will improve the production rate and allow satisfying consumer demand. With small number of image samples, the system is capable of automating assembly line related work for classifying bananas along with sufficient overall accuracy. The noninvasive method will also be used to classify other clustered fruits or horticultural crops in the future. The system can either replace or aid human operators who can focus their efforts on fruit selection. The combined merits of RGB and HSI (hyperspectral imaging) for classification of bananas were highlighted in the present study; they have possible application as a model for classification of several types of horticultural produce. The multi-input model’s quick processing time can be a useful and handy technique in the farm field during postharvest procedures. Via a combination of CNN and MLP applied to data collected using RGB and hyperspectral imaging, the multi-input model reliably recognizes bananas with an accuracy level of 98.4 percent as well as an F1-score of 0.97. The AI algorithm predicted the size (large, medium, and microscopic) and perspective (front or rear half) of banana classes with 99 percent accuracy. In comparison to previous studies that simply employed RGB imaging, the presented model revealed the value of integrating RGB imaging and HSI approaches.

Concentrated solar power is an alternative renewable energy technology that converts solar energy into electrical energy by using a solar concentrator and a solar receiver. Computational fluid dynamics have been used to numerically design concentrated solar power. This is a powerful numerical analysis approach that is widely used in energy and environmental engineering applications. In this paper, we review previous work on the applications of computational fluid dynamics in the design of concentrated solar power technology. We performed a bibliometric analysis of journal articles relevant to applications to analyze the current trend of utilization of computational fluid dynamics in these technologies. Then, we conducted a comprehensive analysis focused on the design of solar dish technology using computational fluid dynamics. Furthermore, we reviewed in detail the optical modeling of solar concentrators and solar receivers. Of the 83 retrieved publications from Scopus database, 80 were journal articles, and only three were review papers. Among these 80 journal articles, only 54 were relevant to this study, and 23 were relevant to solar dish technology. The documents were analyzed according to their number of citations, journal sources, and keyword evolution and network map. The information presented in this paper is useful to further recognize the contributions of computational fluid dynamics to the development of concentrated solar power, particularly to solar dish technology. In addition, we also discuss the challenges and future research directions to make solar energy a more sustainable source of renewable energy.

'Correction to: Multi-criteria Groundwater Resource Evaluation in Tagum City, Philippines through TOPSIS and GWQI-GIS Analysis of Structural and Water Quality Indicators' published in 'Computer Sciences for Sustainable Development'