Crop Recommendation using Soil Properties and Weather Prediction Dataset
Description
The dataset is comprehensive, encompassing various key factors critical to machine learning-based crop recommendation systems. The soil properties dataset includes detailed information such as specific locations identified by latitude and longitude coordinates, soil pH, soil color, surface soil composition, electrical conductivity, and a range of soil macro and micronutrients. These factors are essential in determining the suitability of different crops to various soil types. The crop type information in the dataset primarily comprises cereals, reflecting the major crops in the regions under study. The climate features dataset includes a broad spectrum of environmental conditions crucial for crop growth. This includes specific location data (latitude and longitude coordinates), as well as seasonal variations such as maximum and minimum temperatures, precipitation levels, humidity, wind speed and direction, surface pressure readings, and cloud cover assessments. These features provide insights into the climatic conditions that crops will experience throughout the growing season. The climate data has been sourced from the National Aeronautics and Space Administration's (NASA) cloud infrastructure. The soil properties and crop type data have been procured from the Ethiopian Agricultural Transformation Agency (ATA), ensuring that the dataset is grounded in reliable and locally relevant agricultural data. Additionally, crop production data has been obtained from the Ethiopian Statistics Service (ESS), providing a historical context for the crop types included in the dataset. The process of data collection was methodical. Initially, soil properties and crop type data were gathered. Following this, climate feature data was collected based on specific years and geographical coordinates (latitude and longitude), with an emphasis on anticipating the conditions for the upcoming season. This structured approach ensures that the dataset is well-rounded, providing a solid foundation for the development of a machine learning-based crop recommendation system.