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Agritech Application: Flutter, TensorFlow, and Firebase Integration for Real-Time Geolocation-Based Crop Recommender System Using Random Forest Classification

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Kheti - Grow More With Less

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Getting Started

This project was created as part of a 12-hour business hackathon, and is built to run on the Flutter stable release.

Important

For projects with Firestore integration, you must first run the following commands to ensure the project compiles:

flutter pub get
flutter packages pub run build_runner build --delete-conflicting-outputs

This command creates the generated files that parse each Record from Firestore into a schema object.

About The Project

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Abstract

The application aims to build a smart marketplace, that connects crop vendors to farmers in India. Information regarding the location of farmers, availability of crops, the weather conditions and the plantation area would empower us to suggest a curated selection of crops, recommend crop cycles and a suitable division of land that would enable the farmer to maximize their overall yield.

Unique Selling Points

  1. Recommends crops to plant depending on the time of the year, their geolocation and other factors.
  2. Suggests suitable vendors according to the the availability of the aforementioned crops.
  3. Helps reduce capital consumption of farmers by offering a multitude of vendors to select from.
  4. We also have an Aadhar Card Verification System in place, for verifying the farmers identity before any transaction takes place.
  5. Benefits the vendors by connecting them directly to a huge and diverse market of farmers.

Methodology

  1. Creating a database for all the vendors and farmers (to securely store their credentials), based on their location data collected from a Google Maps API integrated directly into the app.
  2. Making a curated selection of crops for the farmer, using suitable machine learning algorithms (Random Forest Classifier).
  3. Verifying farmer’s identity (Aadhar Card) using a CNN (Convolutional Neural Network) model.
  4. Creating a smart marketplace to connect all the crop vendors and farmers by filtering vendors based on location, and sorting based on price.

Social Impact

Our digital footprint of all farmers and vendors will make it easy to integrate government welfare schemes, thus ensuring maximum reach to each of the parties through our app. As the agriculture industry in India grows exponentially, more and more farmers are starting to adopt technology. Thus we are providing a digital solution to not only reduce the capital required for farming, but also maximize the yield using the power of machine learning.

Market Competitors

Our competitors are mostly small-scale government schemes which lack full-implementation or fall prey to corruption. Thus, we intend to put forward a digital solution to minimize the hassle of procurement of right seeds, choosing the right vendor/getting the best prices and attaining maximum yield for the farmers.

Tech Stack

  1. Flutter/Dart
  2. Firebase
  3. Tensorflow and Keras (CNN Documentation/Aadhar Check Model)
  4. Scikit-Learn (Random Forest Crop Recommender System)
  5. Flask
  6. Tools - FlutterFlow (UI/UX) and Postman (API Testing).
  7. API's - Google Maps in Flutter and Real-Time Crop Prices Data.

In-App Screenshots

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Agritech Application: Flutter, TensorFlow, and Firebase Integration for Real-Time Geolocation-Based Crop Recommender System Using Random Forest Classification

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