A novel hybrid edge detection technique: ABC-FA

Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods. The results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.Publisher's Versio

Assessment of the nutritional value of selected wild food plants in Türkiye and their promotion for improved nutrition

Türkiye represents one of the world’s biodiversity hotspots, containing over 11,000 species of plants, with an estimated 10% being edible. Wild food plants, especially in rural areas, are collected and eaten or sold in local markets, complementing people’s diets, and represent a source of additional income for foraging households. Yet, the use of wild food plants is declining, with both their dietary and cultural values being undermined. Wild food plants can be used as a healthy dietary alternative to imported and ultra-processed foods, particularly as the Turkish population increasingly suffers from diet-related diseases. Using a unique and innovative approach to mainstream biodiversity for food and nutrition, wild food plants from five different regions of Türkiye were analyzed to determine their nutrient composition, and to evaluate their contribution not only to diets and nutrition, but to promoting a more sustainable food system. Examples are presented of how the approach was put into practice and how action was taken to (i) strengthen the evidence of the nutritional value of wild food plants; (ii) use this knowledge to shape new policies and identify emerging markets for food biodiversity; and, (iii) improve awareness of consumers, using capacity building and farmer training, gastronomy, and cultural events

Saliency detection based on hybrid artificial bee colony and firefly optimization

Saliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous, recent studies reveal that optimization-based methods provide relatively accurate and fast solutions for such problems. This paper presents a novel unsupervised hybrid optimization method that aims to propose reasonable solution to saliency detection problem by combining the familiar artificial bee colony and firefly algorithms. The proposed method, HABCFA, is based on creating hybrid-personality individuals behaving like both bees and fireflies. A superpixel-based method is used to obtain better background intensity values in the saliency detection process, providing a better precision in extracting the salient regions. HABCFA algorithm is capable of achieving an optimum saliency map without requiring any extra mask or training step. HABCFA has produced superior performance against its basis algorithms, artificial bee colony, and firefly on four known benchmark problems regarding convergence rate and iteration count. On the other hand, the experimental results on four commonly used datasets, including MSRA-1000, ECSSD, ICOSEG, and DUTOMRON, demonstrate that HABCFA is adequately robust and effective in terms of accuracy, precision, and speed in comparison with the eleven state-of-the-art methods