Green & Smart Agriculture
When green energies were introduced to this world, there was a race to integrate this trend into industries. One of the neglected areas was agriculture. Remote places have inadequate energy sources. Hence, they depend on fossil fuels to run generators. There is a vital need to light farm fields or start an irrigation pump. While nowadays IoT systems are rapidly evolving, and we can finally integrate green energies with IoT sensors. This will enable us to monitor water tank level, soil moisture, weather forecast apps, light condition, and many other variables. Cloud storage can save valuable data for smart farming solutions. Managing the quality of water, soil, and air, or how much water is required by plants, are some of the advantages of this scheme. To illustrate that, imagine that based on the weather forecast the next day there is sufficient rainfall in the region. In a traditional system, farmers irrigate the field daily but in a smart one, based on soil moisture and weather condition, cloud-based AI will choose the most efficient option to boost efficiency. Another prime example would be that, if there are different kinds of renewable energy sources, the system can process the sensor's data to choose which one is more convenient to use at the moment. This method is vital to ensure self-sufficiency in agriculture production, which will lead to increased energy efficiency in food production and distribution. I have used HOMER Pro for designing a hybrid micro-grid and the results were interesting. In some parts of my country (IRAN) wind energy resources outperformed solar power and in some cases neither of them was efficient. Connecting to a power grid might be more logical. Therefore, a smart system based on green energy, IoT sensors, cloud-based servers, and evaluating variables with an AI machine would help the agriculture industry remarkably. This platform can decide when and how much water is needed, and when and how much light and energy are needed. It can also decide which power source is better.