Potato Disease Classification - Training, Rest APIs, and Frontend to test.

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Develop a system that can classify and detect leaf diseases in potato plants based on deep learning. This system can help farmers and agricultural researchers to get accurate and fast diagnose results of disease in plants, especially in potato plant.

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Potato Disease Classification Setup for Python: Install Python (Setup instructions) Install Python packages pip3 install -r training/requirements.txt pip3 install -r api/requirements.txt Install Tensorflow Serving (Setup instructions) Setup for ReactJS Install Nodejs (Setup instructions) Install NPM (Setup instructions) Install dependencies cd frontend npm install --from-lock-json npm audit fix Copy .env.example as .env. Change API url in .env. Setup for React-Native app Initial setup for React-Native app(Setup instructions) Install dependencies cd mobile-app yarn install cd ios && pod install && cd ../ Copy .env.example as .env. Change API url in .env. Training the Model Download the data from kaggle. Only keep folders related to Potatoes. Run Jupyter Notebook in Browser. jupyter notebook Open training/potato-disease-training.ipynb in Jupyter Notebook. In cell #2, update the path to dataset. Run all the Cells one by one. Copy the model generated and save it with the version number in the models folder. Running the API Using FastAPI Get inside api folder cd api Run the FastAPI Server using uvicorn uvicorn main:app --reload --host 0.0.0.0 Your API is now running at 0.0.0.0:8000 Using FastAPI & TF Serve Get inside api folder cd api Copy the models.config.example as models.config and update the paths in file. Run the TF Serve (Update config file path below) docker run -t --rm -p 8501:8501 -v C:/Code/potato-disease-classification:/potato-disease-classification tensorflow/serving --rest_api_port=8501 --model_config_file=/potato-disease-classification/models.config Run the FastAPI Server using uvicorn For this you can directly run it from your main.py or main-tf-serving.py using pycharm run option (as shown in the video tutorial) OR you can run it from command prompt as shown below, uvicorn main-tf-serving:app --reload --host 0.0.0.0 Your API is now running at 0.0.0.0:8000 Running the Frontend Get inside api folder cd frontend Copy the .env.example as .env and update REACT_APP_API_URL to API URL if needed. Run the frontend npm run start Running the app Get inside mobile-app folder cd mobile-app Copy the .env.example as .env and update URL to API URL if needed. Run the app (android/iOS) npm run android or npm run ios Creating the TF Lite Model Run Jupyter Notebook in Browser. jupyter notebook Open training/tf-lite-converter.ipynb in Jupyter Notebook. In cell #2, update the path to dataset. Run all the Cells one by one. Model would be saved in tf-lite-models folder. Deploying the TF Lite on GCP Create a GCP account. Create a Project on GCP (Keep note of the project id). Create a GCP bucket. Upload the tf-lite model generate in the bucket in the path models/potato-model.tflite. Install Google Cloud SDK (Setup instructions). Authenticate with Google Cloud SDK. gcloud auth login Run the deployment script. cd gcp gcloud functions deploy predict_lite --runtime python38 --trigger-http --memory 512 --project project_id Your model is now deployed. Use Postman to test the GCF using the Trigger URL. Inspiration: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions Deploying the TF Model (.h5) on GCP Create a GCP account. Create a Project on GCP (Keep note of the project id). Create a GCP bucket. Upload the tf .h5 model generate in the bucket in the path models/potato-model.h5. Install Google Cloud SDK (Setup instructions). Authenticate with Google Cloud SDK. gcloud auth login Run the deployment script. cd gcp gcloud functions deploy predict --runtime python38 --trigger-http --memory 512 --project project_id Your model is now deployed. Use Postman to test the GCF using the Trigger URL. Inspiration: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions

Built a Potato Leaf Disease Classifier web app using Streamlit and deployed it on Heroku. Internally, the model is built using a simple Convolutional Neural Network Architecture to classify potato leaf diseases.

Developed a deep learning model using TensorFlow and Convolutional Neural Networks to classify disease images of potato plants, including early blight, late blight, and overall plant health in agriculture. Model achieved an impressive accuracy of 97.8%, empowering farmers with precise treatment applications to enhance crop yield and quality.

Established web app employs Python's Flask Framework for frontend structure, linking with a backend ML model to classify disease types in potato plants based on leaf images and the application of Convolutional Neural Networks.

This is a Potato Disease Classification Mobile Application Using Flutter Framework and Convolutional Neural Network Artificial Intelligent Model. this is a simple and tech base solution in agriculture sector. this application is free to download Hope you like it. link below. 👇👇👇

Potato Disease Classification using CNN

Web application to detect potato plant leaf diseases 🍃

Development of a deep learning project in the field of agriculture We will create a simple image classification model that will categorize Potato Leaf Disease using a simple convolutional neural network architecture.

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For this project, a Convolutional Neural Network (CNN) model was employed for the task of potato disease detection.

This project focuses on the classification of potato plant diseases using a Convolutional Neural Network (CNN) model implemented in TensorFlow. The primary goal is to accurately identify early blight and late blight diseases from images of potato leaves.

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This project aims to develop an automated potato disease classification system using deep learning. By leveraging a Convolutional Neural Network (CNN), the model classifies high-resolution images of potato leaves into different categories, including healthy and diseased plants (early blight, late blight). The system is deployed using Flask and proc

Potato Disease Classification using CNN.Dataset is available on kaggle.

This web app is designed to classify potato plants into one of three categories: early blight, late blight, or healthy

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The potato disease classification project using deep learning involves training a neural network on a dataset of images of healthy and diseased potato plants to accurately classify different diseases affecting potato crops, by learning and recognizing patterns and features in the images.

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Potato Disease Classification Using CNN and Tensorflow

Detect and classify potato diseases using a Convolutional Neural Network (CNN) with 100% accuracy an effective solution for agricultural health monitoring.

This research presents a hybrid deep learning framework combining MobileNet V2 with LSTM, GRU, and Bidirectional LSTM for classifying various potato diseases. The study explores the performance of different architectures to determine the optimal configuration for accurate disease categorization.

🥔 Potato Disease Classification (Deep Learning Project) : Using CNN Architecture and TensorFlow

Potato Disease Classification done by using deep-learning and for the sake of knowing various diseases caused to Potato plant and for quick remedial action. Link of the website 👇

Using deep learning to predict the type of potato plant's diseases to help prevent economic loss in agriculture