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Your search keyword '"Vijaya Choudhary"' showing total 7 results
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7 results on '"Vijaya Choudhary"'

1. Automatic classification of cowpea leaves using deep convolutional neural network

Author

Vijaya Choudhary, Paramita Guha, Giovanni Pau, Rajesh Kumar Dhanaraj, and Sunita Mishra

Subjects
Binary classification, Deep neural network, Novel data collection, Cowpea Leaves, CNN-denseNet121, Productive agriculture, Agriculture (General), S1-972, Agricultural industries, HD9000-9495
Abstract

This paper uses a binary classification to discriminate cowpea leaves from other/weed leaves. Cowpea is one of the nutritional crops, but research on cowpea is negligible worldwide. Hence our experiments are purely based on cowpea leaves to increase the productivity of the high fiber consumed. An automatic model implementation has yet to be done on the cowpea leaves dataset. Therefore, our work is essential for researchers in this field. In such cases, various state-of-the-art algorithms are available, but our approach appears competitive against existing ones. The overall analysis of the complete results we have cited in the paper in table format. The images have been collected from the cowpea field of The Indian Council of Agricultural Research (ICAR), New Delhi and dataset constructed in the lab. The plants are grown under suitable environmental conditions, and images have been collected with a standard DSLR camera. In the proposed CNNs model, data (images) of cowpea leaves have been collected from a Research farm. For all collected data, we have made labeled data (Databank) to utilize cowpea leaves for further research, and then we applied three convolutional neural networks to classify cowpea leaves for smart agriculture. The experimental results show that the DenseNet121 realizes a detection performance with the highest accuracy on the CLDC. We have also used two more CNN architectures to identify cowpea leaves from the weed, and a comparative study has been made and explored in the paper. DenseNet121 methods give an accuracy of 86.12% (training dataset) and 88.89% (testing dataset), respectively.

Published
2023
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6. Design and Development of Graphical User Interfaces for Detection of Forest Fires at early stages using Open-Source Technologies

Author

Paramita Guha, Kailash Chand, Jaskaran Singh, Chandan Taluja, and Vijaya Choudhary

Subjects
Signal processing, Computer port, business.industry, Computer science, Fetch, Python (programming language), Software, Open source, Development (topology), Software engineering, business, computer, computer.programming_language, Graphical user interface
Abstract

In this paper, a Graphical User Interface (GUI) is designed and developed using Python programming language for detection of forest fires at early stages, which is efficient, cost-effective, flexible, secure and uses open-source technologies. The user-friendly Graphical User Interface is interfaced directly with the computer port to fetch the data with the help of Python modules, performs an analysis on the data and predict whether there is a situation of fire or not and displays the status on the Graphical User Interface. Data is also stored in an Excel file for further analysis. The possibility to detect forest fire situation at its early stages makes this GUI a valuable software in environmental disasters and research applications. Numerical results along with future work have been discussed in this paper.

Published
2021
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7. Automatic Detection of Cowpea leaves Using Image Processing and Inception-V3 Model of Deep Learning

Author

Vijaya Choudhary, Kuldeep Tripathi, Sunita Mishra, and Paramita Guha

Subjects
education.field_of_study, Contextual image classification, business.industry, Emerging technologies, Deep learning, Population, Image processing, Agricultural engineering, Multiclass classification, Binary classification, Artificial intelligence, business, Transfer of learning, education
Abstract

India is an agriculture-based country, and most of the population depends on agriculture for livelihoods. Cowpea plays a vital role to maintain healthy life because of its nutritious parameters. However, there is an urgent need to implement new technologies using Artificial Intelligence to enhance the productivity of crops and mitigate the cost, effort and loss of the agronomist. We have proposed a model based on deep learning algorithms to identify Cowpea leaves. For experiments, images were captured from the ICAR-NBPGR farm. We use TensorFlow and Keras platform under the umbrella of Deep Learning. Such methodology permits us 1) reorganization of the cowpea leaves based on image classification and 2) to estimate their weight. Inception model is type of application utilizes CNN based architecture to complete problems related to either binary or multiclass classification, that will again moderate by using transfer learning technique. We have initiate model for binary classification of Cowpea leaves with other leaves (Mango).

Published
2021
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