Sun drying of fish is an important method of fish preservation throughout the world. Due to its availability and product specific flavor and texture, dried fish has become an important part in the diet of people living particularly in the coastal region of many Asian countries including Bangladesh. Product type is considerably wide depending on the season, availability of fish, infrastructure and technical know-how of the processors. These are mainly processed from marine fishes that are caught by the artisanal fishermen and marketed throughout the country by multistep marketing channel up to reaching to the consumer. Due to the importance of the product in terms of market volume as well as its major role in the socio-economic condition of millions of people of the country, the sector needs proper attention by appropriate authorities to ensure quality and safety of the product produced and marketed. In this paper, different methods of sun drying practiced traditionally in the coastal region of Bangladesh and associated marketing systems are reviewed. It is expected that training of processors on Sanitation Standard Operating Procedures and monitoring by appropriate government body would be necessary to improve quality and simultaneously ensure safety of sun-dried fish produced in the coastal region of Bangladesh. Res. Agric., Livest. Fish.5(3): 381-390, December 2018

Dengue endemicity has become regular in recent times across the world. The numbers of cases and deaths have been alarmingly increasing over the years. In addition to this, there are no direct medications or vaccines to treat this viral infection. Thus, monitoring and controlling the carriers of this virus which are the Aedes mosquitoes become specially demanding to combat the endemicity, as killing all the mosquitoes regardless of their species would destroy ecosystems. The current approach requires collecting a larva sample from the hatching sites and, then, an expert entomologist manually examining it using a microscope in the laboratory to identify the Aedes vector. This is time-consuming, labor-intensive, subjective, and impractical. Several automated Aedes larvae detection systems have been proposed previously, but failed to achieve sufficient accuracy and reliability. We propose an automated system utilizing ensemble learning, which detects Aedes larvae effectively from a low-magnification image with an accuracy of over 99%. The proposed system outperformed all the previous methods with respect to accuracy. The practical usability of the system is also demonstrated.