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Energy is one of the important parts of our life. As there is decline in fossil fuels and increasing demand for energy an alternate energy source is required which is renewable energy source like solar, wind etc. So, we use solar panels which trap the energy from the sun and produce electricity, and this energy is used for agriculture purpose like to run water pumps and to meet other energy requirements in agriculture. Due to rotation of earth the stationery solar panel will receive energy only for smaller duration so to overcome this we use dual axis tracking system which rotates solar panel according to direction of sun and helps in producing more solar energy. Agriculture is one of the major contributing sectors to the economy of a country and it requires automation and advanced technology so that it helps farmers in producing more yield and better crops. So, in agriculture continuous monitoring of soil and water level is required so we can automate this which helps the farmers where the device continuously monitors and depending upon the moisture level of the soil the water pumps get on automatically and we can use this for different crops and set threshold depending upon the crop type. And we can also integrate this idea with IOT technology for improvements. By this we create sustainable energy indirectly producing sustainable environment.
Energy is one of the important parts of our life. As there is decline in fossil fuels and increasing demand for energy an alternate energy source is required which is renewable energy source like solar, wind etc. So, we use solar panels which trap the energy from the sun and produce electricity, and this energy is used for agriculture purpose like to run water pumps and to meet other energy requirements in agriculture. Due to rotation of earth the stationery solar panel will receive energy only for smaller duration so to overcome this we use dual axis tracking system which rotates solar panel according to direction of sun and helps in producing more solar energy. Agriculture is one of the major contributing sectors to the economy of a country and it requires automation and advanced technology so that it helps farmers in producing more yield and better crops. So, in agriculture continuous monitoring of soil and water level is required so we can automate this which helps the farmers where the device continuously monitors and depending upon the moisture level of the soil the water pumps get on automatically and we can use this for different crops and set threshold depending upon the crop type. And we can also integrate this idea with IOT technology for improvements. By this we create sustainable energy indirectly producing sustainable environment.
Agriculture is the main source of income, food, employment, and livelihood for most rural people in India. Several crops can be destroyed yearly due to a lack of technical skills and changing weather patterns such as rainfall, temperature, and other atmospheric parameters that play an enormous role in determining crop yield and profit. Therefore, selecting a suitable crop to increase crop yield is an essential aspect of improving real-life farming scenarios. Anticipating crop yield is one of the major concerns in agriculture and plays a critical role in global, regional, and field decision-making. Crop yield forecasting is based on crop parameters and meteorological, atmospheric, and soil conditions. This paper introduces a crop recommendation and yield prediction system using a Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends crops and forecasts crop yield accurately and promptly. The proposed model used a Probabilistic Neural Network (PNN) for crop recommendation and the Extreme Learning Machine (ELM) method for the crop yield forecasting process. The HMFO algorithm was used to improve the forecasting rate of the ELM approach. A wide-ranging simulation analysis was carried out to evaluate the HMFO-ML model, showing its advantages over other models, as it exhibited a maximum R2 score of 98.82% and an accuracy of 99.67%.
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