Paper Title
Smart Sensors For Monitoring and Optimization of Vermicompost Quality: Integration of Machine Learning Models for Artificial Intelligence-Based Backyard Farming

Abstract
Vermicomposting is a sustainable method that can be used for backyard farming because it enriches soil and manages organic waste. However, the constraints of manual monitoring and environmental factors make maximizing vermicompost quality in small-scale settings difficult. This project proposes a method to improve backyard farming's vermicomposting quality and efficiency using smart sensors and deep learning models. The researcher used Arduino operated by a 12-volt battery and solar controller and RS485 Modbus Soil Sensors that provide real-time data for process optimization by continuously monitoring essential parameters, including temperature, moisture, pH, and soil nutrients like potassium, phosphate, and nitrate. The researcher used a 12V water pump and rubber hose for the water system. Through artificial intelligence in conjunction with vermicomposting, this technology produces high-quality vermicompost by facilitating automated monitoring, data-driven decision-making through the water system, and enhanced nutrient breakdown. This innovative technique has the potential to revolutionize backyard farming practices and improve soil health, productivity, and sustainability in small-scale farming environments. Keywords—Vermicomposting, Arduino, artificial intelligence, soil sensor