Popular GitHub Repositories for Rice

Computer Vision and Image Processing algorithms implemented using OpenCV, NumPy and MatPlotLib, for UOM's EN2550 Fundamentals of Image Processing and Machine Vision Module ❄

本项目翻译自 Liz Rice 的 Learning eBPF，这是一本比较系统的 eBPF 入门书籍。

Harness the power of machine learning to forecast rice and wheat crop yields per acre in India, aiming to empower smallholder farmers, combat poverty and malnutrition, utilizing data from Digital Green surveys to revolutionize agriculture and promote sustainable practices in the face of climate change for enhanced global food security.

A record of the road segmentation term project for the Statistical Machine Learning course at Rice University

FarmFlow is an AI-powered advisory platform designed to assist rice farmers in optimizing their agricultural practices. By combining expert farming knowledge with real-time insights, FarmFlow provides personalized recommendations on crop health, resource use, and cost-saving strategies.

A repository for the research article titled "RiceCloud: A Cloud integrated Ensemble learning based Rice leaf Diseases prediction System".

This project provides a valuable tool for farmers, enabling them to manage rice plant diseases effectively and improve crop yield. The combination of advanced deep learning techniques and mobile technology offers a practical solution for real-world agricultural challenges.

水稻叶片病害识别模型是在谷歌的siglip2-base-patch16-224模型基础上微调得到的图像分类模型，用于检测和分类水稻叶片的病害。该模型基于SiglipForImageClassification架构构建，有助于早期识别植物病害，从而更好地进行作物管理。

This project provides a valuable tool for farmers, enabling them to manage rice plant diseases effectively and improve crop yield. The combination of advanced deep learning techniques and mobile technology offers a practical solution for real-world agricultural challenges.

A Comprehensive Study of Rice Production and Price in Bangladesh: Geospatial Visualization and Predictive Modeling Techniques.

The Detection Rice Disease application is a tool designed for farmers to identify rice diseases using image processing and deep learning. This AI-powered technology offers more accurate disease classification than visual inspection.