Crop Suitability Prediction

Introduction:

In the pursuit of a simpler and more fulfilling life, an increasing number of individuals are turning to farming and gardening as not just activities but as cherished hobbies. Embracing the soil, cultivating plants, and tending to crops have become more than just pastimes; they symbolize a desire to reconnect with nature, adopt sustainable practices, and find solace in the simplicity of nurturing life.

The project aims to make farming and gardening more accessible to the common man.

Project Objective:

This project aimed to create an accessible solution that brings farming to the everyday person, leveraging machine learning for crop suitability prediction. The machine learning model predicts the most appropriate crop to grow based on factors such as region, temperature, rainfall, altitude, and irrigation status.

The project included a web application that not only allowed users to input environmental and land details for crop prediction but also featured a dealer registration system. Sellers of seeds, fertilizers, pesticides, insecticides, and farming tools could register their businesses on the platform, enabling users to access a comprehensive list of suppliers. The web app stored user details, products, and prices, providing a one-stop solution for farmers to acquire necessary farming equipment.

A MySQL database was used to manage registered users, dealers, and product inventories. Python powered the decision tree classification model, which was the core of the prediction engine. Additionally, an Android application was developed to further enhance user experience by providing real-time guidance based on updated inputs.

This solution not only empowers individuals with personalized crop suggestions but also connects them with relevant product suppliers, thereby making farming more approachable and efficient.

Proposed System:

• User Registration: Individuals interested in cultivating crops or plants register on the website. They provide details such as temperature, rainfall, and soil pH.
• Tailored Recommendations: The system processes user inputs and generates personalized crop recommendations, considering the specified conditions. Users receive detailed information about the suggested crops via SMS.
• Companion App: An accompanying mobile app offers specific care instructions for the recommended crops. This feature ensures that users have access to comprehensive guidance throughout the cultivation process.
• Dealer Registration: Dealers specializing in agricultural materials like seeds, fertilizers, and tools can register on the platform. They can offer their products to users as needed.

Deliverables:

A comprehensive and user-friendly website will be delivered, allowing users to:

• Register and express interest in growing specific crops or plants.
  
• Receive SMS notifications containing detailed information about the recommended crops.
• Download a companion app providing precise care instructions for the selected crops.
  
• Enable dealers to register and sell necessary agricultural materials.

Conclusion:

This solution offers a practical and user-friendly approach to modern farming by combining predictive analytics with an integrated platform for purchasing essential agricultural products. By lowering the barriers to farming knowledge and connecting users with the resources they need, this project provides a scalable and innovative tool that can benefit both novice and experienced farmers. Future enhancements could involve improving the prediction model’s accuracy and expanding the dealer network to cover a wider range of agricultural needs.