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A discernible correlation emerged between global bridge disasters and bridge deterioration in recent years. To assist bridge inspectors in conducting deterioration identification, this paper presents a Pilgrimage Walk Optimization (PWO) algorithm inspired by Taiwan's unique Matsu bobee custom. The search behavior of the PWO algorithm simulates the gathering of devotees following Matsu's palanquin and their collective movements based on folk belief activities, including divination block casting, pilgrimage, the leisure ceremony, crawling beneath the palanquin, palanquin robbing, and the return palanquin ceremony. Analysis results show that the PWO outperformed contemporary metaheuristic algorithms on various multidimensional benchmark functions and was effectively applied to image segmentation for multiple types of bridge deterioration collected by an unmanned aerial vehicle. In the increasing number of engineering problems requiring edge computing to process big data, the proposed PWO algorithm is a low-cost and robust method with fault tolerance. • Presenting the innovative Pilgrimage Walk Optimization (PWO) algorithm, a revolutionary metaheuristic approach. • The new algorithm is tested on benchmark functions and applied to optimize image segmentation. • PWO algorithm outperforms contemporary metaheuristic algorithms in terms of achieving optimal solutions. • Exploring diverse bridge deterioration patterns auto-segmented using metaheuristic clustering algorithms. • PWO algorithm holds potential applications in AIoT technology and automation within the construction industry. [ABSTRACT FROM AUTHOR]

Phalaenopsis orchids are one of the most important exporting commodities for Taiwan. Most orchids are planted and grown in greenhouses. Early detection of orchid diseases is crucially valuable to orchid farmers during orchid cultivation. At present, orchid viral diseases are generally identified with manual observation and the judgment of the grower's experience. The most commonly used assays for virus identification are nucleic acid amplification and serology. However, it is neither time nor cost efficient. Therefore, this study aimed to create a system for automatically identifying the common viral diseases in orchids using the orchid image. Our methods include the following steps: the image preprocessing by color space transformation and gamma correction, detection of leaves by a Unet model, removal of non-leaf fragment areas by connected component labeling, feature acquisition of leaf texture, and disease identification by the two-stagemodel with the integration of a randomforestmodel and an inception network (deep learning) model. Thereby, the proposed system achieved the excellent accuracy of 0.9707 and 0.9180 for the image segmentation of orchid leaves and disease identification, respectively. Furthermore, this system outperformed the naked-eye identification for the easily misidentified categories [cymbidium mosaic virus (CymMV) and odontoglossum ringspot virus (ORSV)] with the accuracy of 0.842 using two-stage model and 0.667 by naked-eye identification. This system would benefit the orchid disease recognition for Phalaenopsis cultivation. [ABSTRACT FROM AUTHOR]

Agricultural activities mainly occur in rural areas; recently, ecological conservation and biological diversity are being emphasized in rural communities to promote sustainable development for rural communities, especially for rural communities in Taiwan. Therefore, since 2005, many rural communities in Taiwan have compiled their own development strategies in order to create their own unique characteristics to attract people to visit and stay in rural communities. By implementing these strategies, young people can stay in their own rural communities and the rural communities are rejuvenated. However, some rural communities introduce artificial construction into the community such that the ecological and biological environments are significantly degraded. The strategies need to be efficiently monitored because up to 67 rural communities have proposed rejuvenation projects. In 2015, up to 440 rural communities were estimated to be involved in rural community rejuvenations. How to monitor the changes occurring in those rural communities participating in rural community rejuvenation such that ecological conservation and ecological diversity can be satisfied is an important issue in rural community management. Remote sensing provides an efficient and rapid method to achieve this issue. Segmentation plays a fundamental role in human perception. In this respect, segmentation can be used as the process of transforming the collection of pixels of an image into a group of regions or objects with meaning. This paper proposed an algorithm based on the multiphase approach to segment the normalized difference vegetation index, NDVI, of the rural communities into several sub-regions, and to have the NDVI distribution in each sub-region be homogeneous. Those regions whose values of NDVI are close will be merged into the same class. In doing so, a complex NDVI map can be simplified into two groups: the high and low values of NDVI. The class with low NDVI values corresponds to those regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005–2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation. [ABSTRACT FROM AUTHOR]