Self-Tuned Controller for Achieving Enhanced Voltage Stability in a Multi-Machine System

Abstract

The paper will provide proof of the efficacy of remote sensors in agriculture as a process control approach using an online laboratory. Data of environmental parameter, such as temperature, humidity, soil moisture and light intensity, were gathered from sensor node system which had been installed in open land of agricultural field. Four machine learning models including ours have been put into going forward and back to the future of agriculture. These are the Simple Linear Regression, Decision Tree, k-Nearest Neighbors, and Support Vector Machine that have been established and examined for their efficacy in predicting and managing agriculture processes using this data you provided. The findings indicated that the Decision Tree method qualified for the best 92% of the accuracy index, while Support Vector Machine produced the accuracy of 90% in their outcome. Neural Network algorithm showed an 88% accuracy, while Simple Linear Regression algorithm trailed with 85% accuracy The result signifies the fact that computer learning software, such as tree of decisions and support schemes can highly be used in improvement of agriculture systems through real-time control and responses. The combining of online remote experiments serves to create a scalable and affordable platform on which agricultural scientists and specialists can work together and make progress in agricultural technology which supports the advancement of more efficient and sustainable food production systems.