Smart Agriculture: Real-Time Environmental Monitoring and Temperature Forecasting Using Machine Learning

Nowadays, farmers face challenges of unpredictability in weather, inadequate control over environmental factors, ineffective use of resources, and therefore poor crop yield and a loss of profits. These include fluctuations in temperature, which affect crop maturity, and drought, which hinders plant growth and output. Their decision- making is constrained due to inadequate timely and accurate information on these circumstances, which leads to inefficient irrigation, fertilizing, and harvesting practices. This project proposes to meet these challenges through a real-time data monitoring system that uses sensors, IoT gadgets, and machine learning approaches. In this system, environmental factors such as humidity, temperature, rainfall, moisture, and light intensity are closely observed through the sensors planted on the farm. An Arduino gathers information and forwards it to a NODEMCU, which transmits it to the web to a Google Sheet using App Script. The information is processed by Orange, the machine learning algorithms including Random Forest, AdaBoost, SVM, Decision Tree, and KNN to predict the ambient temperature. This system provides accurate temperature predictions and a textualized rendition of the current climate conditions that allow farmers to be more objective in their decisions concerning the need for irrigation, crop protection, and utilization of the resources available. The integration of IoT and machine learning enhances farm management practices, which might increase production, decrease loss, and increase the efficiency of resource utilization in farming, which are great challenges farmers encounter today.