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Plant-Seedling Classification Using Transfer Learning-Based Deep Convolutional Neural Networks

Keshav Gupta, Rajneesh Rani, Nimratveer Kaur Bahia

Source Title: International Journal of Agricultural and Environmental Information Systems (IJAEIS)11(4)

ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781799806967|DOI: 10.4018/IJAEIS.2020100102

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MLA

Gupta, Keshav, et al. "Plant-Seedling Classification Using Transfer Learning-Based Deep Convolutional Neural Networks." IJAEIS vol.11, no.4 2020: pp.25-40. http://doi.org/10.4018/IJAEIS.2020100102

APA

Gupta, K., Rani, R., & Bahia, N. K. (2020). Plant-Seedling Classification Using Transfer Learning-Based Deep Convolutional Neural Networks. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 11(4), 25-40. http://doi.org/10.4018/IJAEIS.2020100102

Chicago

Gupta, Keshav, Rajneesh Rani, and Nimratveer Kaur Bahia. "Plant-Seedling Classification Using Transfer Learning-Based Deep Convolutional Neural Networks," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 11, no.4: 25-40. http://doi.org/10.4018/IJAEIS.2020100102

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Abstract

The ever-growing population of this world needs more food production every year. The loss caused in crops due to weeds is a major issue for the upcoming years. This issue has attracted the attention of many researchers working in the field of agriculture. There have been many attempts to solve the problem by using image classification techniques. These techniques are attracting researchers because they can prevent the use of herbicides in the fields for controlling weed invasion, reducing the amount of time required for weed control methods. This article presents use of images and deep learning-based approach for classifying weeds and crops into their respective classes. In this paper, five pre-trained convolution neural networks (CNN), namely ResNet50, VGG16, VGG19, Xception, and MobileNetV2, have been used to classify weed and crop into their respective classes. The experiments have been done on V2 plant seedling classification dataset. Amongst these five models, ResNet50 gave the best results with 95.23% testing accuracy.