Improving Crop Yields and Resource Efficiency in Organic Farming Using Advanced Optimization Techniques: A Machine Learning Approach

The use of optimization techniques in modern agriculture is growing, and these methods frequently introduce dangerous substances into the food chain, posing serious health hazards. In an effort to fulfill the rising demand for food, non-organic agricultural techniques are displacing traditional systems, which rely on farmers’ expertise and natural practices. But these actions have a negative impact on the environment and human health. This effort intends to address these concerns by developing a prediction model to improve agricultural processes and boost the production of organic food. The model uses statistical models and machine learning algorithms to estimate ideal crop development circumstances, including humidity, rainfall, and soil demands. This helps farmers overcome challenges such as unforeseen weather and crop diseases. To guarantee precise forecasts and sustainable agricultural methods, the technique entails methodical data gathering, preprocessing, model training, testing, and fine-tuning. The results show that applying these optimization techniques may result in a 15% increase in crop output, a 20% reduction in water consumption, and a 10% reduction in fertilizer use. These improvements assist farmers in improving health outcomes, reducing their negative effects on the environment, and boosting their revenue. The study shows how advanced analytics might transform agriculture, support data-driven decision-making, and aid in providing food for a growing global population in a sustainable manner.