SIP | Smart Hydroponic System: An AI and IoT-Driven Solution for Controlled Environment Vertical Farming 🌊🌿

Harnessing the power of artificial intelligence and IoT, these applications revolutionize decision-making processes for indoor farmers. The Crop Recommendation tool suggests suitable crops based on environmental data collected from hydroponic systems, while the Fertilizer Recommendation module identifies nutrient deficiencies or excesses, offering precise recommendations for improvement. Additionally, the Plant Disease Prediction application swiftly diagnoses plant ailments from uploaded images, equipping farmers with actionable insights for disease management in controlled environments

MOTIVATION 💪

• Farming is one of the major sectors that influences a country’s economic growth.

• In country like India, majority of the population is dependent on agriculture for their livelihood. Many new technologies, such as Machine Learning and Deep Learning, are being implemented into agriculture so that it is easier for farmers to grow and maximize their yield.

• In this project, I present a website in which the following applications are implemented; Crop recommendation, Fertilizer recommendation and Plant disease prediction, respectively.

  • In the crop recommendation application, the user can provide the water data from their side and the application will predict which crop should the user grow.

  • For the fertilizer recommendation application, the user can input the water data and the type of crop they are growing, and the application will predict what the water lacks or has excess of and will recommend improvements.

  • For the last application, that is the plant disease prediction application, the user can input an image of a diseased plant leaf, and the application will predict what disease it is and will also give a little background about the disease and suggestions to cure it.

DATA SOURCE 📊

• [Crop recommendation dataset ] (custom built dataset)
• [Fertilizer suggestion dataset] (custom built dataset)
• Disease detection dataset

Built with 🛠️

Python, Flask, HTML, CSS, Javascript, MERN, Pytorch, Pandas, Numpy, Matpotlib, Excel, RestAPI, Seaborn

How to use 💻

• Crop Recommendation system ==> enter the corresponding nutrient values of your water.

• Fertilizer suggestion system ==> Enter the nutrient contents of your water and the crop you want to grow. The algorithm will tell which nutrient the water has excess of or lacks. Accordingly, it will give suggestions for buying fertilizers.

• Disease Detection System ==> Upload an image of leaf of your plant. The algorithm will tell the crop type and whether it is diseased or healthy. If it is diseased, it will tell you the cause of the disease and suggest you how to prevent/cure the disease accordingly.

Future Enhancements

-CSS Optimization: The CSS code could be better organized, as it is currently scattered across inline styles and external files.

-Frontend Design: The user interface could be significantly enhanced to make it more visually appealing and user-friendly.

-Data Collection: Gathering additional data through web scraping could improve the system's accuracy and reliability.

-Plant Image Dataset: Expanding the dataset with more plant images would make the disease detection component more robust and generalized.

-Code Modularization: Instead of relying on Jupyter Notebooks, the code could be refactored into modular scripts to enhance scalability and maintainability—a practice I aim to follow in future projects.