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Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Exploring agricultural image data through deep learning for visual information analysis : A study on Cabbage (Brassica Oleracea var. Capitata) farming

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pp. 501–513Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1291XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1291
Pages:
501–513

Abstract

This article outlines a methodology for developing and evaluating efficient image classification models using Convolutional Neural Networks (CNNs). It begins with meticulous network architecture design and training on a dataset comprising 9000 images of Cabbage, Weeds, and empty areas in cabbage plants, with the aim of achieving accurate image classification based on features and patterns. The paper conducts a detailed comparative analysis with established models such as AlexNet and ResNet, as well as modified versions of AlexNet and ResNet, employing performance metrics such as accuracy, precision, recall, and the F1 score. This comprehensive evaluation highlights the proficiency of the developed models and their relative effectiveness. Real-world datasets featuring fragmented agricultural plot images validate the models in practical scenarios, affirming their accuracy and reliability in agricultural data analysis. This article is helpful for the implementation of CNN in the automation of weeding in cabbage crops.

Keywords

Convolutional neural networkResNetAlexNetWeeds

Subject Classifications

68T45 Machine vision and scene understanding

References

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