This is the repository containing team OverFeat's submission to CVPPP 2020's Wheat Detection Challenge (2/2245)

AI Application that can predict the most suitable crops to grow in particular farm based on various parameters.

A deep learning-based system using Convolutional Neural Networks (CNNs) to detect diseases in arecanut crops through image classification, enabling early diagnosis and sustainable agricultural practices.

The Cotton Disease Detection System is an AI-powered web application that helps farmers and agricultural experts identify diseases in cotton plants through image analysis.

A machine learning-based system recommending crops based on soil, climate, and environmental conditions to optimize agricultural yields.

🛠️ Prototype for Hack Revolution 2025. PS: AI-Powered Crop Yield Prediction & Optimization. Apex Harvest — A web-based AI platform that empowers farmers with data-driven crop yield predictions & personalized optimization tips, without requiring IoT devices or expensive hardware. 🌾

Guava Disease Classification using DenseNet-Reloaded and ADOPT optimizer. Achieves 99.73% accuracy in Anthracnose, Fruit Fly, and Healthy Guava detection.

A Machine Learning project for Plant Disease Prediction using Random Forest, Streamlit, and Google Colab. Includes interactive UI and detailed analysis.

A web-based plant disease detection application using TensorFlow CNNs trained on 79,000+ images, with REST API integration for real-time diagnosis and latency-optimized prediction.

Hi, I’m Lucy — a data-driven professional passionate about applying **AI and data analysis** to solve real-world challenges in both **agribusiness** and other industries. E-mail:lucyadhiambo89@gmail.com

🌾 Predict and optimize crop yields with Apex Harvest, an AI-powered platform designed to support farmers through data-driven insights.