Khairun Yahya

THREATENED FISHES OF THE WORLD: Labeo bata (Hamilton, 1822) (Cypriniformes: Cyprinidae)

Minor carp Labeo bata is an endangered fish species in Bangladesh. This fish is one of the commercially and nutritionally valuable food fish in Asian countries including Bangladesh, India and Pakistan. L. bata is an important target species for small scale fishermen in Bangladesh who use a variety of traditional fishing gears. However, natural populations are seriously declining due to loss of habitat, siltation in the river basin, indiscriminate killing of fry and fingerlings, water pollution by industrial and domestic waste, agrochemicals and overexploitation. This paper recommends the sustainable conservation measures of this endangered fish species in Bangladesh and neighboring countries. CODEN RIBAEG ISSN 1330-061X (print), 1848-0586 (online)

THREATENED FISHES OF THE WORLD: Botia dario (Hamilton, 1822) (Cypriniformes: Cobitidae)

Bengal loach Botia dario is a valuable food fish in South Asian countries including Bangladesh, Bhutan, India and Nepal. However, the wild popula- tion is declining due to heavy harvest, habitat loss and its ecological chang- es. This paper suggests actions for the conservation of the lasting isolated populations of B. dario in Asian countries.

Tracking Multiple Fish in a Single Tank Using an Improved Particle Filter

Studies on tracking fishes have become a popular research endeavour in recent years. Many methods have been used to track fishes by integrating microchips in fishes, using infra-red cameras, image processing and motion sensor. The use of particle filter in the process of tracking has been widely used by researchers. Particle filters is used to track people, fluid movement and animals. In this paper, the particle filter algorithm is improved to track multiple fish in a fish tank. The aim is to identify every fish trajectories and fish target location for further analysis. The main challenge is to ensure that the correct fish are tracked and the algorithm manages to identify specific fish even if they overlaps with each another. The objective of the study is to improve the existing particle filter to track multiple fish in a single fish tank. The improved algorithm contains an additional cache which stores the object’s position to estimate the next potential move of the fish. The result is evaluated by comparing existing algorithm without the enhancement with the improved algorithm. Besides, suggestions in improving the particle filter will also be discussed in this paper.

Recognition of fish based on generalized color Fourier descriptor

Recognizing objects using computational methods have become a popular research endeavor among researchers. In this paper, the recognition of fish based on GCFD (Generalized Color Fourier Descriptor) is introduced. The features are extracted using the GCFD technique which represents the image in a frequency domain. By analyzing the frequencies, the non-related information (backgrounds, not required lines or borders) are identified by performing some spectrum changes on the frequencies. The required objects in this study are the fish. In other words, the frequencies corresponding to the fish are maintained while other frequencies are removed from the frequency domain. After removing the non-related frequencies, the frequency domain is inversed in order to obtain the required image for further image processing. GCFD is used as a descriptor to extract the features of the fish as it is invariant to rotation and translation. A cultured fish tank installed with high-end video cameras is required to record the video from side and top views. Koi fish are chosen due to their active swimming behavior, variety of colors and easy-to-adapt habitat in the water. The evaluation of the technique is based on Bhattacharyya Distance. Some improvements were obtained in the recognition rate using the GCFD compared with existing color descriptors. The improvement can lead to better classifications of objects.

Modelling of Fish Swimming Patterns Using an Enhanced Object Tracking Algorithm

Modelling of fish swimming patterns has long been a challenge for the aquaculture community. In this proposed work, the modeling of fish swimming patterns and water condition such as pH, temperature and dissolved oxygen in fish tanks are analysed using network cameras and water sensors. An enhanced object tracking algorithm consisting of a combination of motion detection and condensation algorithm is applied to detect the change in speed and vector movement of the fishes in the viewing field. In addition, this research proposes an intelligent real-time monitoring tool to provide alert signals of water quality and healthy level of fishes.

Tracking Multiple Fishes Using Colour Changes Identification and Enhanced Object Tracking Algorithm

Tracking multiple fishes using computational methods have become a research endeavor among researchers. Different concepts have been taken such as installing water sensors and video cameras to identify movement speed, colours, shapes and swimming patterns displayed by the fishes. In this research, an enhanced algorithm consisting of motion detection algorithm and condensation algorithm is proposed. This algorithm is further integrated with colour changes identification technique which considers the changes in colour on fishes. This is to identify overlapping fishes and to detect the distance between the camera and the fishes in the water. In our case study, a cultured fish tank installed with water sensors to monitor water pH, dissolved oxygen and temperature is set up together with two network cameras. Koi fishes are chosen due to their active swimming behaviour, variety of colours and easy-to-adapt habitat in the water. A real-time prototype system which models the fish swimming pattern consisting of the enhanced algorithm and the colour changes identification is developed.

Using Non-supervised Artificial Neural Network for Determination of Anthropogenic Disturbance in a River System

The study of river water quality plays an important role in assessing the pollution status and health of the water bodies. Human-induced activities such as domestic activities, aquaculture, agriculture and industries have detrimentally affected the river water quality. Pinang River is one of the important rivers in Balik Pulau District that supplies freshwater for human consumption. A total of 442 physical and chemical parameters data of the Pinang River, Balik Pulau catchment were analysed to determine the sources of pollutants entering the river. Non-supervised artificial neural network (ANN) was employed to classify and cluster the river into upstream, middle-stream and downstream zones. The monitored data and non-supervised ANN analysis demonstrated that the source of nitrate was derived from the upper part of the Pinang River, Balik Pulau while the sources of nitrite, ammonia and ortho-phosphate are predominant at the middle-stream of the river system. Meanwhile, the sources of high total suspended solid and biological oxygen demand were concentrated at the downstream of the river.

Enhanced Object Tracking in Real-Time Environment Using Dual Camera

Research in object tracking has become a popular research domain among researchers. The use of video cameras to capture motion images instead of static images poses a great challenge to researchers in terms of speed and accuracy of the object tracking and detection. In this paper, fish are used in the experiment due to its active swimming behaviour which emulates the movement of many living objects such as humans or animals. The main problem faced in the tracking process is that the fishes swim in various directions and angles. It is difficult to track and identify a specific fish in a school of fish in most cases. Besides, the fish may also appear to be overlapping one another. There exists several object tracking systems in the market but the focuses are mainly on surveillance. Besides, there is still room for improvement in the tracking process. Therefore, this paper focuses on using an additional camera for tracking fish in real-time environment. The evaluation of the tracking process consists of using the same video with an additional camera positioned above the fish tank. Results show improvement when an additional camera is used to obtain additional information mainly the trajectory patterns of the fish which contributes to bringing a higher accuracy of the tracking system.

THREATENED FISHES OF THE WORLD: Ompok pabda (Hamilton, 1822) (Siluriformes: Siluridae)

The endangered catfish, Ompok pabda, is one of the foods high in nutritional value in Asian countries but its natural populations have declined due to over-exploitation and various ecological changes in its natural habitats leading to an alarming condition and deserving high conservation importance. This paper recommends the actions for the sustainable conservation of the remaining isolated population of O. pabda in Asian countries.

THREATENED FISHES OF THE WORLD: Rhinomugil corsula (Hamilton, 1822) (Mugiliformes: Mugilidae)

Department of Fisheries, Faculty of Agriculture, University of Rajshahi, Rajshahi 6205, Bangladesh School of Biiological Sciences and Centre of Excellence for Marine and Coastal Studies, Universiti Sains Malaysia, Penang 11800, Malaysia 3 Faculty of Fisheries, Kagoshima University, 4-50-20 Shimoarata, Kagoshima 890-0056, Japan Department of Zoology, Anando Mohan College, National University, Gazipur 1704, Bangladesh