A Comprehensive Data Driven Approach on Crop Yield and Fertilizer Efficiency

Abstract

India’s economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil’s alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. The system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue.