Soil NPK Prediction using Enhanced Genetic Algorithm

Agriculture is one of the primary sources of livelihood and revenue in the globe that satisfies human needs. To gain healthy crop yields, the farmers should give importance to the soil health. The objective of this work is to propose an Enhanced Genetic Algorithm (EGA) model for soil nutrient prediction using the factors like temperature, humidity, pH, and rainfall. The algorithm calculates the Nitrogen, Phosphorus, and Potassium (NPK) values based on these inputs and uses rank-based selection for fitness selection. Enhanced crossover and mutation techniques are also done to increase its performance. In order to accurately identify the NPK values in soil, the enhanced genetic algorithm has been used. The algorithm's accuracy is evaluated using a dataset of soil, and it is found to be higher than that of conventional genetic algorithm. By providing more detailed and timely soil nutrient data, this enhanced genetic algorithm has the potential to help farmers optimize their crop yield.