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Sharma, Vinod
- Classification of Spices using Machine Learning Techniques
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Authors
Affiliations
1 M.Tech. Student, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
2 PhD Research Scholar, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
3 Sr. Assistant Professor, Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu and Kashmir, IN
4 Professor, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
1 M.Tech. Student, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
2 PhD Research Scholar, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
3 Sr. Assistant Professor, Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu and Kashmir, IN
4 Professor, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
Source
International Journal of Knowledge Based Computer System, Vol 10, No 1 (2022), Pagination: 27-32Abstract
Machine learning (ML) has played a significant role in pattern recognition including fruits and vegetables classification. In this paper, comparative analysis of various ML techniques have been carried out for the identification of Spices. For the current work, ML techniques namely Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) have been undertaken. The main aim of the current study is to find out the most appropriate ML approach for Spices recognition. The experimental study has been performed on primary dataset of Spices. This dataset consists of 1000 images of five different Spices including clove, green cardamom, cinnamon, black pepper and curry leaf. The performance of the ML techniques have been analyzed on the basis of four parameters i.e. accuracy, precision, recall and f1-score. Out of five implemented ML models, best performance has been predicted by SVM approach with accuracy of 94.5%, precision of 95%, and recall of 94% with f1-score of 0.95..Keywords
Decision Tree, K-Nearest Neighbor, Machine Learning, Spices Recognition, Support Vector Machine
References
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- Mango Leaf Diseases Detection using Deep Learning
Abstract Views :174 |
PDF Views:0
Authors
Affiliations
1 M.Tech. Student, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
2 PhD Research Scholar, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
3 Sr. Assistant Professor, Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu and Kashmir, IN
4 Professor, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
1 M.Tech. Student, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
2 PhD Research Scholar, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
3 Sr. Assistant Professor, Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu and Kashmir, IN
4 Professor, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, IN
Source
International Journal of Knowledge Based Computer System, Vol 10, No 1 (2022), Pagination: 40-44Abstract
Diseases and pests cause great economic loss to the mango industry every year. The detection of various mango diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This research paper is an attempt to provide the timely and accurate detection and identification of mango leaf diseases. Convolutional Neural Networks are end-to-end learning algorithms which perform automatic feature extraction and learn complex features directly from raw images, making them suitable for a wide variety of tasks like image classification, object detection, segmentation etc. In the proposed study, we develop a Convolutional Neural Networks based model for detection and classification of mango leaf diseases at the initial stages. Data augmentation is performed on a collected dataset. We applied data augmentation techniques like rotation, translation, reflection and scaling. Convolutional Neural Networks model has been trained on the augmented data for detection and classification of mango leaf diseases. The proposed CNN based model attains 90.36% of accuracy. The results validate that the proposed method is effective in detecting various types of mango leaf diseases and can be used as a practical tool by farmers and agriculture scientists.Keywords
Convolution Neural Network (CNN), Crop, Deep learning, Image classification, MangoReferences
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