Implementation of Smart Agriculture using ESP32-based IoT and Machine Learning for Monitoring Crop

Smart agriculture has become a sustainable solution to enhance the productivity of crops through the use of Internet of Things (IoT) technology and machine learning. In this project Smart Agriculture Implementation through the ESP-32 based IoT and machine learning to monitor crops and provide recommendations are presented, thus connecting real-time field monitoring to cloud connectivity and intelligent decision-making. The given system uses ESP32 microcontroller as the central processing unit, connected to various sensors including DHT11 (temperature and humidity), soil-related sensors (via LDR / light intensity), PIR sensor and flame sensor to constantly control the condition of the environment and field. A solar panel with charge controller is used to provide power to the system and is therefore energy efficient with its ability to be installed in remote agricultural fields. The ESP32 captures sensor data which are shown on a 16X2 LCD display and at the same time sent to the cloud via Blynk IoT to allow farmers to monitor their farms or farms in real time. The obtained IoT data is pre-processed, segmented and features extracted to discard noise and derive suitable parameters. A LSTM (Long Short- Term Memory) model is then used to examine the temporal trends in the realtime and historical data of agriculture. Such a model does not only foretell the future of the environment, but also proposes the most appropriate crop as per the prevailing temperatures, humidity, and light intensity among other factors. The new system will improve precision farming through data-driven crop advice, early warning, and constant surveillance, which will lead to resource saving and high-quality yields. The system is costeffective, scalable, and appropriate to be used in smart farming because it integrates renewable energy, IoT and machine learning to enhance this system.