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AgriMarket is an open-source digital marketplace that connects farmers and buyers in Somalia. It allows farmers to list their products, while buyers can browse, purchase, and manage orders, promoting direct trade and supporting Somalia's agricultural growth.
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.
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.
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.
Our “RSCMS” is more than just a technical project – it reflects a practical solution to a real-life problem that affects millions of people involved in farming, processing, and consuming rice. Through this project, we aim to gain experience in full-stack development, working in teams, and solving real-world problems with meaningful impact.
SkyAI is a web-based application designed to provide AI-driven crop valuation and personalized insurance policy advice for the agricultural business. This project uses image processing and deep learning to analyze satellite images of farmland and generate detailed valuation reports, risk assessments and claims management
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.