IoT-based optical sensor network for precision agriculture

Precision agriculture is a modern agricultural method that employs state-of-the-art technology and data-driven decision-making to increase yields. In this context, there is much potential to improve agricultural operations by integrating Internet of Things devices and optical sensors. The accurate data extraction and analysis provided by sensor networks and Machine Learning based tracking devices are in high demand. This study aims to promote intelligent farming while lowering agricultural risks. Insects and other pathogens can cause plant illnesses, which may decrease yield output if not handled promptly. Therefore, in this research, we provide a novel Artificial Swarm Fish Optimized Naïve Bayes technique to monitor the soil's quality and guard against diseases that affect cotton leaves. The present study uses Internet of Things devices with optical sensors to track several metrics vital to crop development and health. These sensors record information about temperature, humidity, light intensity, chlorophyll content, and other important environmental variables. The acquired data is then wirelessly communicated to a centralized server, where the suggested approach is used to process and analyze the data. After identifying the infection, through an Android app. Soil parameter like humidity, temperature, and moisture may be presented with the chemical level in a container using the Android app. The power source and chemical sprinkler system may be managed by turning the relay on or off using an Android app. The experimental results show that the suggested strategy performs better when compared to conventional methods of illness detection.