Seaweed Growth Detection in Aquaculture Environment Using Simple Linear Iterative Clustering Method

Estimating the total biomass of cultivates in aquaculture plantations (fisheries, mussel plants, seaweed farms and compound sites) remains to be an issue for the industry and the researchers alike. There has been a diverse array of approaches towards this issue, like using markers, manually stapling the leaflets, weighting the actual mass of the organism and calculating the total mass by extrapolation. Seaweed growth detection is a subset of this problem. Our goal is to introduce a solution by automatically detecting the ratio of the target object in images of seaweed taken from an underwater environment. Researchers/operators then can evaluate the total mass of seaweed. This study aimed to function as a decision support system. The system is built based on an image segmentation algorithm named Simple Linear Iterative Clustering (SLIC) which is a kind of superpixel segmentation. This paper conveys the results obtained from our approach towards the seaweed growth detection, elaborates on the usage and feasibility of our solution in seaweed sites and showcase the economic impact in the industry. Other dimensions of the growth detection methods in current practice for seaweed growth is also discussed, such as lack of automation in the current best-practices while focusing on the difficulties accompanying this status-quo.

A Novel Organism Lactobacillus wasatchensis: Growth, Detection, Gassing Defects in Cheese, Control Strategy and Future Research Opportunities: A Review

An obligate heterofermentative, Lactobacillus wasatchensis has been recently isolated from an aged Cheddar cheese produced in Logan, Utah. The potential of this organism in causing gassing defects in aged cheese has raised concern among cheese manufacturers. The recent attention on this organism is attributed to its economic impact due to low-quality cheese. This comprehensive review provides the details about Lb.wasatchensis characteristics, geographical distribution and effect of various physical and chemical factors such as heat treatment, carbohydrate utilization, pH, salt tolerance and growth temperature. Lb. wasatchensis utilize ribose as a primary source for its growth, however, it can slowly utilize galactose resulting in gas generation. The details of testing methods along with suggestions for future research on improving these techniques using a phage as a selective medium are provided in this review. Recent research developments for controlling the growth of Lb. wasatchensis, as well as potential research opportunities are summarized in this review.

Stress-Free Detection Technologies for Pig Growth Based on Welfare Farming: A Review

The detection of pig growth and monitoring of abnormal behaviors are key steps in pig breeding management. Using conventional methods to obtain information on growth and abnormal behavior causes stress to pigs, directly affects the number of live pigs for market, and decreases the quality of the pork. Moreover, this approach requires considerable labor, reduces economic returns, and does not meet the requirements of high-welfare farming. Compared to the conventional methods for obtaining growth parameters and data on abnormal behaviors, modern information technology provides a new method for stress-free growth detection and behavior monitoring in farmed pigs. This article first summarizes the importance of body size, body mass, and abnormal behaviors as well as the correlations among these factors. For the research on growth detection and behavior monitoring based on computer vision, radio frequency identification (RFID) and sensor technology, methods of detecting increases in body size and body mass and methods of monitoring abnormal behaviors are summarized separately. Through computer-computer vision technology, we found that the data sampling for growth and abnormal behaviors of the pigs was achieved without contact monitoring but, rather, occurred at the expense of complex data calculation and a higher illumination requirement during data collection. However, with the development of depth camera technology and improved product performance, technology based on high-precision depth cameras reduces the amount of data processing and complexity, making it possible to obtain real-time data on pig growth and abnormal behaviors. Moreover, with the advantages of no contact and no stress, the method conforms to the requirements of welfare farming. Keywords: Abnormal behaviors, Stress-free detection, Welfare farming.