FertiForecast: Identification of Fertilizer Based on NPK Levels using Machine Learning Algorithms

For many people in India, especially those living in rural areas, agriculture is their main source of income. Approximately 50% of the labour force in the nation is employed by it, either directly or indirectly. Fertilizers playa crucial role in modern agriculture by enhancing soil fertility and promoting optimal plant growth. They provide essential nutrients to plants, including nitrogen, phosphorus, potassium, and micronutrients. To boost crop yields, commercial fertilizers are sprayed on agricultural crops. The majority of farming took place on tiny family farms with little usage of chemicals before to the 1950s. Since then, the usage of chemical fertilizers in contemporary agricultural methods has paralleled with the move toward larger corporate farms. Farmers may lack knowledge about the various types of fertilizers available, their formulations, and the appropriate selection for specific crops and soil conditions. This can lead to the use of inappropriate or imbalanced fertilizers. Machine learning algorithms can play a significant role in fertilizer prediction in agriculture, offering several advantages that contribute to more efficient and sustainable fertilizer management. ML algorithms analyze diverse datasets, including soil characteristics, weather patterns, crop types, and historical yield data. This data-driven approach enhances decision-making by providing insights into the factors influencing fertilizer needs.