CropSentry is an intelligent, mobile-first application designed to help farmers and gardeners detect plant diseases and pests using machine learning. The app leverages high-resolution images of plants to identify potential issues, providing valuable insights to improve crop health and yields.
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Disease and Pest Detection The app uses Machine Learning models to classify and identify diseases and pests in plants by analyzing high-resolution images. Real-time Image Processing: Users can upload pictures of their crops, and the app will process and identify the disease/pest with high accuracy. Provides detailed information on the detected disease/pest, including possible causes and solutions.
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Mobile-First Design The app is optimized for mobile devices, ensuring a smooth user experience even in field conditions where access to computers may be limited. Available on Expo Go, ensuring quick development and testing on mobile devices.
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User Profiles Users can create personal profiles, storing their uploaded images and disease/pest detection history. Track progress over time by revisiting past diagnoses and checking previous reports.
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Educational Resources The app provides a Literacy section with helpful information on various plant diseases and pests, giving users access to resources for better plant care. "Read More" functionality to access in-depth guides and advice on plant health.
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Seamless User Interaction Capture Screen: Users can take a photo of a plant directly within the app for immediate disease detection. Prediction Results: After processing the image, the app provides prediction results, displaying the most likely disease or pest, along with its confidence score.
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User-Friendly Interface The app features an intuitive design with bottom navigation for easy access to key features like Home, Capture, Profile, and Literacy.
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Machine Learning Model Built using TensorFlow Lite to deploy the model on mobile devices for efficient and fast inference. The model is trained on a variety of crop diseases and pests, allowing it to recognize a wide range of plant issues.
Frontend: React Native with Expo
Backend: FastAPI (for model inference and API endpoints)
Machine Learning: TensorFlow Lite (for model inference on mobile)
Database: User data and history can be stored on a cloud database (if implemented)