Combines a pre-trained model with a user-friendly Streamlit interface for quick preliminary disease assessment, aimed at farmers with limited technical experience.
Agri Vision is a simple crop health classification application that uses a pre-trained deep learning model to identify possible diseases from crop leaf images.
The application is designed to give farmers a quick preliminary assessment of crop health and provide simple guidance based on the detected condition.
All the work was done by me and my teammate, Ajohn, we just did it on the school computer so the PR was required.
https://github.com/user-attachments/assets/d34747e4-0481-42de-b469-03e264ff1c39
Agri Vision currently supports:
The model can identify 15 different crop conditions across these crops.
The application follows a simple process:
Agri Vision uses a pre-trained TensorFlow model stored in the following file:
crop_disease_model.h5
The model expects input images with a size of:
224 x 224 pixels
The model predicts one of 15 possible crop conditions.
These include:
For the best results, users should:
Agri Vision is a simple crop health classification application that uses a pre-trained deep learning model to identify possible diseases from crop leaf images.
Similar projects matched by category, topics, and programming language.
Harness Engineering is a methodology for improving coding agent outputs by carefully crafting the environment around them—providing curated context, tools, and executable constraints that encode an organization’s nonfunctional requirements and cumulative lessons.
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
mimic captures traffic from any iOS or web app and automatically generates a Python client library that lets you call the app's API like a regular library.