Analyze the adaption of organic fertilizer in aquaculture by using embedded technology approaches

Abstract

The Paper titled “Analyzing the adaption of organic fertilizer in aquaculture by using embedded technology” is a comprehensive review that delves into the transformative role of machine learning and IoT in Aquaculture. This study also deals with assessing water purifying, Pond Plankton growth, and specific Ph and TDS determination for fish farming for some selective ponds. The Paper proceeds with a detailed analysis of machine learning and IoT applications across various aquaculture domains. As our country is a riverine country, we get most of the fish from here. Which we can eat and sell to earn domestic and foreign exchange. So, we need to protect these fish. But day by day the population is increasing. As a result, new problems arise and new requirements are needed. As the increased population fulfills the protein requirement people have developed fish farming for the fish's natural growth. The nutrition level of the fish in the river is very good but over time, the condition of the river is deteriorating due to which the amount of fish farming in the pond is increasing day by day. Fish needs extra care to get good quality fish. Chemical fertilizers are used to increase the growth rate of fish but we want to increase the growth rate naturally. Plankton is a type of organic food for fish. Use organic fertilizers to increase plankton growth. The use of modern technology will be a successful development and fulfill people’s needs also there are various drawbacks in cage-culture. If we recover this problem with a modern system, it’ll be very helpful for this aqua species and enhance our economic state. We implemented and evaluated several models to predict plankton growth, achieving varying degrees of accuracy. Our Random Forest Classifier model from accuracy of

100%. In machine learning, the Logistic Regression accuracy rates of 99.30%, the K- Nearest Neighbors (KNN) model yielded an accuracy of 95.12%, the Gradient Boost

accuracy rates of 100%, while the Support Vector Machine (SVM) model reached an accuracy of 61.85%. These results indicate that machine learning models, particularly Random Forest and Gradient Boost, can provide highly reliable plankton growth predictions based on the sensor data collected. The high accuracy rates of our models demonstrate the potential for such IoT-based systems to offer precise and efficient water quality monitoring solutions, contributing to the Adoption of organic fertilizer in aquaculture by using embedded technology.