When we talk about AI and IOT in farming, we definitely cannot ignore the importance of machine learning as machine learning models can process complex data, make accurate prediction and improve the productivity and efficiency of hydroponic farming.

In my research project  "Predicting Humidity Levels in Greenhouses using Regression Models," I used machine learning to models to process complex data.

Machine learning plays a significant role in enhancing hydroponic systems by analyzing data from sensors. The key benefits of ML in hydroponics:

• Optimizing Resource Utilization: Machine learning models analyze data from sensors that track parameters like temperature, humidity, pH, and nutrient levels (e.g., Nitrogen, Phosphorus, Potassium).

• Anomaly Detection: ML models can identify anomalies in real-time by continuously monitoring sensor data. When abnormalities are detected, the system can either make automatic adjustments or notify operators to prevent damage to crops.

• Crop-Specific Recommendations: The machine learning model can be trained to predict which crops are best suited for particular conditions.

• Data Preprocessing: The dataset used for training the model can be preprocessed to handle missing and inconsistent data.

• Correlation Insights: It can provide correlation insights too