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README.md

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-license: cc-by-4.0
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-Please cite the article below:
+# RiceNet Dataset: High-Quality Image Dataset for Rice Variety Classification
+
+## Overview
+
+The RiceNet Dataset is a meticulously curated collection of rice grain images designed to facilitate the classification of five distinct rice varieties using deep learning techniques. This dataset underpins the RiceNet model, a Deep Convolutional Neural Network (DCNN) architecture developed to enhance the accuracy of rice variety identification, addressing challenges in agriculture and trade related to seed misclassification and grain adulteration.
+
+## Dataset Description
+
+* **Varieties Included**: The dataset encompasses images of five rice varieties: Jehlum, Mushkibudji, SR-1, SR-2, and SR-4.
+
+* **Image Acquisition**: High-resolution images were captured under controlled conditions to ensure consistency and quality.([ResearchGate][1])
+
+* **Data Augmentation**: To bolster the dataset's robustness, augmentation techniques such as zooming, rotation, horizontal flipping, and shifting were employed.
+
+* **Format**: Images are stored in standard formats (e.g., JPEG or PNG) and organized into directories corresponding to each rice variety.
+
+## Applications
+
+* **Agricultural Research**: Assists in the development of automated systems for accurate rice variety classification, aiding in breeding programs and crop management.
+
+* **Trade and Quality Control**: Supports the detection of grain adulteration, ensuring quality assurance in rice trade.
+
+* **Educational Purposes**: Serves as a resource for academic projects and research in computer vision and agricultural informatics.
+
+## Performance Benchmarks
+
+The RiceNet model, trained on this dataset, achieved a classification accuracy of 94%, outperforming traditional machine learning approaches:
+
+* **HOG-SVM**: 66.0%
+
+* **SIFT-SVM**: 65.33%
+
+* **HOG-LR**: 62.67%
+
+* **SIFT-LR**: 65.0%([Researcher Life][2])
+
+* **HOG-KNN**: 54.0%([Researcher Life][2])
+
+* **SIFT-KNN**: 52.0%
+
+Additionally, pre-trained models like InceptionV3 and InceptionResNetV2 achieved accuracies of 84% and 81.33%, respectively.
+
+## Usage
+
+Researchers and practitioners can utilize this dataset to train and evaluate models for rice variety classification. The dataset's structure is conducive to integration with popular deep learning frameworks such as TensorFlow and PyTorch.
+
+## Citation
+
+If you use this dataset in your research, please cite the following paper:
+
+```
 Nusrat Mohi Ud Din, Assif Assad, Rayees Ahmad Dar, Muzafar Rasool, Saqib Ul Sabha, Tabasum Majeed, Zahir Ul Islam, Wahid Gulzar, Aamir Yaseen,
 RiceNet: A deep convolutional neural network approach for classification of rice varieties,
 Expert Systems with Applications,
@@ -11,6 +58,18 @@ Volume 235,
 121214,
 ISSN 0957-4174,
 https://doi.org/10.1016/j.eswa.2023.121214.
-(https://www.sciencedirect.com/science/article/pii/S0957417423017165)
-Abstract: The cultivation of desired grain varieties holds immense significance as about 67% of the world’s population is associated with the agriculture sector. Unknowingly sowing the wrong variety of seeds may lead to a colossal waste of effort and money. Furthermore, the growing issue of rice grain adulteration in high-quality rice poses a threat to the trust of rice importers and exporters. While traditional methods are expensive, laborious, and error-prone, Computer Vision provides a good alternative that constitutes a current, advanced technology for image processing and data evaluation that holds tremendous promise and potential. In this research study, five varieties of rice grains including Jehlum Sr-1, Mushkibudji, Sr-2, and Sr-4 were collected from local grain and were used for research analysis. A computer vision system “RiceNet” contingent upon Deep Convolutional Neural Network (DCNN) framework has been designed for ameliorating the accuracy of identifying five unique groups of rice grain varieties. Deep Learning (DL) based pre-trained architectures including InceptionV3 and InceptionResNetV2 models were also adopted for classifying five specific groups of rice species. To optimize model parameters and alleviate back-propagation error during training, the Adam optimizer with a learning rate (lr) of 0.00003 has been employed to fine-tune the pre-trained InceptionV3 and ResNetInceptionV2 models. The proposed RiceNet architecture and pre-trained models were also compared with traditional ML approaches of HOG-SVM, SIFT-SVM, HOG-Logistic Regression(HOG-LR), SIFT-Logistic Regression(SIFT-LR), HOG-KNN, and SIFT-KNN for rice grain classification. With these experimentations at hand, it was observed that our proposed model “RiceNet” outperformed other approaches in similar computer vision tasks. The prediction accuracy outcome for the test dataset by HOG-SVM, SIFT-SVM, HOG-LR, SIFT-LR, HOG-KNN, and SIFT-KNN models were 66.0%, 65.33%, 62.67%, 65.0%, 54.0%, and 52.0% respectively. RiceNet, InceptionV3 and ResNetInceptionV2 have the best prediction accuracy of 94%, 84% and 81.333%. The remarkably high success rate of DCNN models makes them highly valuable and can be extended to endorse an integrated grain identification system that can operate in real-world situations.
-Keywords: Rice grain; Deep learning; Machine learning; InceptionV3; InceptionResNetV2
+```
+## License
+
+This dataset is available for public use under the [Creative Commons Attribution 4.0 International License].
+
+## Contact
+
+For questions or collaborations, please contact Rayees Ahmad Dar at darrayes@gmail.com.
+
+---
+
+This README provides a comprehensive overview of the RiceNet Dataset, facilitating its use in various applications related to rice variety classification.
+
+[1]: https://www.researchgate.net/publication/373222937_RiceNet_A_deep_convolutional_neural_network_approach_for_classification_of_rice_varieties?utm_source=chatgpt.com "RiceNet: A deep convolutional neural network approach for classification of rice varieties | Request PDF"
+[2]: https://discovery.researcher.life/article/ricenet-a-deep-convolutional-neural-network-approach-for-classification-of-rice-varieties/b2cabc927db33d348ba3c79f0b609dbe?utm_source=chatgpt.com "RiceNet: A deep convolutional neural network approach for classification of rice varieties - R Discovery"