Soil Type Prediction Using Machine Learning and Deep Learning Models for Multi-domain Applications

This project presents Soil Type Prediction Using Machine Learning and Deep Learning Models for multi-domain applications, aimed at improving agricultural decision-making. The system is built using a Raspberry Pi integrated with a USB web camera, DHT11 sensor, soil moisture sensor, pH sensor, LCD display, and buzzer. The camera captures soil images, which are processed using machine learning and deep learning models to classify different soil types.In addition to image-based classification, sensors are used to monitor environmental and soil conditions. The DHT11 sensor measures temperature and humidity, the soil moisture sensor detects wet and dry conditions, and the pH sensor determines soil acidity levels. Based on the combined analysis of image data and sensor readings, the system predicts suitable crops for the identified soil type. The LCD displays real-time values and prediction results, while the buzzer provides alerts in abnormal conditions.This system enhances accuracy in soil classification, supports better crop selection, and improves agricultural productivity. It can be effectively used in smart farming and environmental monitoring applications.