Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning

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 technical skills changing weather patterns such as rainfall, temperature, other atmospheric parameters that play an enormous role determining crop yield profit. Therefore, selecting suitable increase essential aspect improving real-life farming scenarios. Anticipating one major concerns agriculture plays critical global, regional, field decision-making. Crop forecasting based on meteorological, atmospheric, soil conditions. This paper introduces recommendation prediction system using Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends forecasts accurately promptly. proposed model used Probabilistic Neural Network (PNN) Extreme (ELM) process. HMFO algorithm was improve rate ELM approach. A wide-ranging simulation analysis carried out evaluate model, showing its advantages over models, it exhibited maximum R2 score 98.82% accuracy 99.67%.