Revolutionizing Agriculture: Integrating IoT Cloud, and Machine Learning for Smart Farm Monitoring and Precision Agriculture

An emerging field of technology centred on using machine learning algorithms in equipment, sensors, and machinery in network-based high-tech farming monitoring systems is “smart farming.” AI and robotics will be introduced to the agriculture sector through IoT, cloud computing, and innovative technologies. Crop growth is affected by repeated farming, and soil nutrients are reduced. The sudden climate change also affects farmland irrigation. Pests that chew on plants chew on crops and bite on crops threaten global food security. Minimizing crop losses and maximizing plant diseases must be identified accurately to ensure plant health. IoT-based technologies such as machine learning (ML) can help you overcome these challenges with real-time visualization and on-demand access to meteorological data. This proposed system will explore integrating intelligent monitoring systems with image processing and how they reshape modern farming practices. The Raspberry Pi is a smart device capable of handling sensor data, integrating irrigation pumps, and collecting images from farmland-like crop images with the help of a Raspberry Pi camera. A deep learning model utilizing the open CV library is initially opened using the captured picture. Preprocessing is the step in which raw image noise is reduced by Wiener filtering. Region-based segmentation and ensemble-based classification algorithms are utilized to categorize plant leaf diseases, crop yields, and insect bite identification. A Raspberry Pi equipped with high-resolution cameras and image processing software can provide detailed images of agricultural fields. The captured image was sent to an IoT cloud topic. The AWS cloud is used for IoT purposes. The farmer subscribed to the topic. The image topic refers to machine learning code working on the Raspberry Pi. Weather prediction uses intelligent sensors like anemometers, humidity, and temperature sensors. The cumulative sensor data trains our ML model for accurate weather prediction. A farmer can use this weather data to decide when and where to irrigate crops, reducing water waste. This information is beneficial to farmers in deciding what additional action to take. The web application was created using the NodeRED language. This tool creates GUIs and server configurations.