Agri-Edge: A Result-Oriented Crop Recommendation System

Abstract

Machine learning can offer farmers tailored recommendations to enhance crop production. The selection of crops for this method is influenced by their climatic traits and volume. Data analytics facilitates the extraction of valuable insights from agricultural databases. Crop data analysis leads to recommendations based on productivity and seasonal factors. By analyzing historical data, weather patterns, soil quality, and other relevant variables, machine learning models can provide important guidance for farming choices. This predictive capability is transforming agricultural management, ensuring that crops are cultivated under ideal conditions and maximizing yields. The challenges posed by a rapidly growing global population, along with the issues associated with climate change, underscore the necessity of dependable crop production forecasting systems. This initiative aims to design and implement an IoT-based monitoring system for environmental conditions that utilizes the DHT11 sensor for temperature and humidity along with a soil moisture sensor. The main objective is to develop a system that gathers real-time information on temperature, humidity, and soil moisture and transmits it wirelessly to an IoT platform for remote observation. The ESP8266 microcontroller acts as a communication intermediary, facilitating smooth data upload and interaction with cloud-based applications. This system allows users to monitor and access environmental parameters remotely through devices connected to the internet, offering valuable insights for applications like smart farming, weather tracking, and home automation. The project involves the integration of sensors, data processing, configuration of an IoT platform, and the development of a user interface for data visualization. The objective of the project is to deliver a reliable and user-friendly solution for efficient environmental monitoring and management through thorough testing and validation.