Crop Recommendation & Soil Health System

A Crop Recommendation and Soil Health System combines IoT sensors with machine learning to analyze soil conditions and recommend a suitable crop.

The system can collect important parameters such as:

• Nitrogen (N)
• Phosphorus (P)
• Potassium (K)
• Soil moisture
• Soil pH
• Temperature
• Humidity

An Arduino-compatible development board or ESP32 can collect the sensor readings and send them to a computer, server or cloud platform for analysis.

A machine learning classification algorithm such as Random Forest can then analyze the collected parameters and classify the most suitable crop based on the trained dataset.

How It Works

Soil Sensors → ESP32/Arduino → Data Collection → Machine Learning Model → Crop Recommendation

For example, different combinations of soil nutrients, moisture and pH can be provided to a trained model. The model analyzes these values and generates a crop recommendation.

Smart Irrigation With Weather Prediction

Water management is an important part of agriculture. A smart irrigation system can combine soil moisture sensors, IoT connectivity and machine learning to improve irrigation decisions.

The system continuously monitors soil moisture and can also use historical weather information such as temperature, humidity and rainfall.

A machine learning regression model can analyze historical data and estimate future environmental conditions or irrigation requirements.

How It Works

Soil Moisture Sensor + Weather Data → IoT Device → Data Processing → ML Regression Model → Irrigation Decision → Water Pump

If the soil becomes dry and the system determines that irrigation is required, the controller can activate a water pump.

The system can also be designed to consider expected rainfall, helping avoid unnecessary watering when rain is likely.

Plant Disease Detection Using Machine Learning

Plant disease detection combines IoT, computer vision and machine learning to identify possible diseases from plant leaves.

Instead of relying only on sensor readings, this project uses a camera to capture images of leaves.

A machine learning model, particularly a Convolutional Neural Network (CNN), can be trained using images of healthy and diseased plants.

An edge-computing device can process the image locally and generate a classification result.

How It Works

Plant Leaf → Camera → Image Processing → CNN Model → Disease Classification → Alert/Result

For example, the system could classify an image as:

• Healthy
• Disease A
• Disease B
• Disease C

The exact classifications depend on the dataset used to train the model.

Predictive Air Quality Monitor

A Predictive Air Quality Monitor combines environmental sensors, IoT connectivity and machine learning to monitor and analyze air-quality data.

Sensors can collect measurements related to pollutants and environmental conditions.

Depending on the hardware, a project may monitor parameters such as:

• CO₂
• NO₂
• PM2.5
• Temperature
• Humidity
• Other gas measurements

The collected data can be stored over time and used to train a machine learning model.

For time-dependent pollution data, time-series machine learning models, including LSTM-based approaches, can be explored to forecast future trends.

How It Works

Air Quality Sensors → IoT Controller → Database → ML Model → Pollution Forecast → Dashboard/Alert

The dashboard can display both current sensor readings and predicted trends.

Block Diagram

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Conclusion

Smart Agriculture & Environmental Tracking is an important application area for combining IoT and machine learning.

A Crop Recommendation System can analyze soil conditions and recommend suitable crops. A Smart Irrigation System can combine soil and weather data to improve irrigation decisions. Plant Disease Detection can use camera images and CNN models to classify plant diseases. A Predictive Air Quality Monitor can analyze historical environmental data and forecast pollution trends.

Together, these projects demonstrate how sensors, IoT connectivity, data collection and machine learning can be combined to build intelligent systems for agriculture and environmental monitoring.