rice-classification

Data Set for Rice Diseases with labels

With its focus on promoting the medicinal varieties of rice and connecting farmers with expert opinions, Staple represents an innovative and impactful approach to promoting sustainable and healthy rice farming practices.

Rice Species Classification using ResNet-18 and a Custom defined CNN, both using PyTorch. If you are getting started with PyTorch, then you may consider cloning this repo and start learning :)

Rice grain image classification using Artificial Neural Networks

Example data and Keras implementation of a deep convolutional neural network described in "Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network".

Using Genetic programming, an Evolutionary Algorithm, to solve and research the problem of Symbolic regression analysis and Rice Classification.

A collection of deep learning models for rice classification using PyTorch, torchvision, and scikit-learn, with performance metrics evaluated using Slurm jobs.

Deep learning model implementation and research projects including AlexNet, InceptionV3, VGG16, ZFNet, Bangla digit recognition, flower classification, rice classification, and exercise detection using TensorFlow and Keras.

Deep Learning based Rice Classification

Deep learning-based rice leaf disease prediction system using CNN and transfer learning techniques. The model classifies rice leaf images into different disease categories to support early disease detection and improve crop management.

Rice variety classification using a simple CNN with TensorFlow/Keras and PyTorch.

Binary classification neural network to distinguish between Jasmine and Gonen rice types using PyTorch. Trained on physical grain characteristics achieving 92-95% accuracy with 18,185 samples. Includes data preprocessing, model training, and evaluation pipeline.

🌾A modern deep-learning workspace for rice variety recognition using two benchmark datasets Aruzz and BDRice.

🌾 Computer Vision and Machine Learning project for classifying 5 rice grain varieties using OpenCV-based feature extraction and KNN, achieving 96.99% classification accuracy.

Rice variety classification using computer vision, supervised learning, and self-supervised learning methods including SimCLR, BYOL, and DINO.