Based on my published research paper, this project uses a "One-vs-All" deep learning approach with EfficientNet B4 to classify cassava leaf diseases. Integrated into an Android app, it helps farmers detect diseases early, supporting sustainable farming and reducing crop losses.

agriculture technology project repo

This project leverages LSTM networks, a type of RNN, to accurately predict fruit and vegetable prices by analyzing a comprehensive dataset, utilizing a refined model adept at navigating the complexities and patterns within agricultural market data.

This script processes an image to detect and estimate the number of kakis (persimmons) by identifying the common orange areas using a Gaussian fit method with least squares estimation.

Potato leaf disease detection using a CNN

Developed a deep learning model using TensorFlow and CNN to accurately identify diseases in potato plants, optimizing crop health and yield. The model distinguishes between diseases such as early blight, late blight, and healthy plants from images with precision.