Abstract
Abstract: Agriculture yield increase and agroindustry goods account for the majority of India's economy. They are the economic backbones of agricultural countries. Yield prediction is a crucial topic in agriculture. Any farmer wants to know what kind of harvest he may expect. Analyze the numerous connected parameters that are utilized to determine the alkalinity of the soil, such as location and pH value. Additionally, third-party apps such as APIs for weather and temperature, type of soil, nutrient value of the soil in that place, quantity of rainfall in that region, and soil composition are used to compute percentages of nutrients such as nitrogen (N), phosphorus (P), and potassium (K). To develop a model, each of these data properties will be evaluated and trained using various machine learning methods. The system includes a model that is precise and reliable in forecasting crop output and providing proper fertilizer ratio recommendations based on atmospheric and soil data. Farmers may also learn which crops are in great demand so that they can be readily produced. This application might be beneficial to all farmers who want to know which crops can be grown in various places or soils in order to optimize their profitability
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call