Abalone Age Prediction Using Machine Learning

Abstract

One of the most common kinds of shellfish are abalone. Their shells are frequently used in jewelry, and their flesh is prized as a delicacy. The cold coastal areas are home to the marine snail known as the abalone. The value of an item is heavily influenced by its age. Cutting the shell through the cone, staining it, and counting the number of rings through a microscope are the boring and time-consuming methods used to determine an abalone's age. The age of abalone is predicted using other, less difficult measurements. Sex, length, diameter, height, whole weight, shucked weight, and shell weight are the physical parameters used. Machine learning is used to make the age prediction. One of the most common kinds of shellfish are abalone. Their shells are frequently used in jewelry, and their flesh is prized as a delicacy. In this work, I think about how to figure out how old an abalone is based on its physical characteristics. Because other approaches to estimating their ages take time, this issue is interesting. As a result, working hours could be saved if a statistical method proves reliable and accurate enough. Depending on the species, an abalone can live for up to 50 years. Their growth rate is primarily influenced by water flow and wave activity-related environmental factors. Those living in sheltered waters typically develop more slowly than those living in exposed reef areas due to differences in the availability of food [1]. Because of this, it is difficult to determine an abalone's age, and their size also depends on whether or not food is available. Additionally, abalone occasionally form so-called "stunted" populations, whose growth characteristics differ significantly from those of other abalone populations.