IoT-based winter season crop prediction using machine learning on Vyas municipality ward No-13, Nepal

Abstract

The agricultural system in Nepal is facing a decline primarily due to its traditional practices. Farmers’ morale is low as they invest significant effort but yield low production, leading to an exodus of young people seeking opportunities abroad. While Nepal is often depicted as an agricultural country in literature, the reality falls short. Improving productivity is crucial, and this can be achieved by enhancing labor efficiency and increasing arable land fertility. To address these challenges, the concept of smart agriculture has emerged, and implementing an IoT-based agricultural system could help manage the workforce and retain skilled youth in their homeland. The main objectives of the study were to develop a monitoring system for arable land using IoT and to predict and recommend suitable crops using machine learning. Due to constraints in time and resources, the study focused on Vyas Municipality Ward no-13 in the Tanahun district. Sensor devices were deployed in the selected ward, and data was collected four times a day at half-hour intervals, monitoring key parameters like Humidity, Temperature, Rainfall, and Soil moisture. In this research, Orange, Ginger, Onion, and Spinach are the main crops predicted and recommended suitable times. In conclusion, the implementation of IoT-based agricultural systems and Machine Learning algorithms can offer valuable insights to farmers, enabling them to make informed decisions regarding crop selection and cultivation timing. This could potentially revitalize Nepal’s agricultural sector and reduce the trend of young people leaving the country in search of better opportunities.