SMART FARMING SYSTEM: Enhancing Water Efficiency, Automated Care, and Disease Detection Using IoT and CNN

The International Water Management Institute states that because of insufficient or excessive water supplies, inefficient irrigation techniques can result in crop output reductions of up to 30%. Inadequate irrigation practices can lead to waterlogging and salt problems on up to 25% of the irrigated land area worldwide. In addition to automatic watering, this study presents a smart farming system designed specifically for Neon Pothos that uses convolutional neural networks (CNNs) for disease detection and real-time monitoring. Neon Pothos' specific requirements are met by the system for this experimental procedure, which makes use of a NodeMCU ESP-12E (8266) microprocessor, a high-precision moisture sensor, and a DHT22 temperature/humidity sensor. CNN makes problem identification simple and efficient to identify diseases. Through a dashboard on a mobile application, users may keep an eye on various parameters and receive push notifications for illness and irrigation alerts. Ad hoc watering is made possible via manual pump control, and powerful algorithms that use the system's database to analyze disease prevalence and water use improve watering plans. This system, which combines technology, algorithms, and user-centered design to promote sustainable farming habits, helps preserve water resources and promotes environmental health.