Capsule Network for Pioneering Disease Detection in Cauliflower Cultivation with Mobile Application Development
Meenalochini M
Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India.
Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India.
Cauliflower cultivation is challenged by various diseases that can severely impact crop health and yield. Traditional disease detection methods are often labour-intensive and prone to errors, highlighting the need for automated and efficient prediction systems. In this study, we propose the use of Capsule Neural Networks (CapsNet) for disease prediction in cauliflower cultivation named as CauliCaps. CapsNet introduces dynamic routing units to capture spatial relationships and hierarchical structures within images more effectively than traditional Convolutional Neural Networks (CNNs). We assemble a comprehensive dataset of labelled cauliflower leaf images, preprocess them for optimal input, and train the CapsNet model using an appropriate loss function and optimization algorithm. Metrics including accuracy, precision, recall, and F1-score are used to compare the model's performance to state-of-the-art techniques. Additionally, we discuss the development of a mobile application based on the trained CapsNet model for real-time disease diagnosis in cauliflower cultivation. This research aims to advance disease prediction in cauliflower cultivation, enabling proactive management strategies and ultimately contributing to improved crop health and sustainability.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Meenalochini M and Amudha P;
Methodology: Meenalochini M;
Writing- Original Draft Preparation: Meenalochini M and Amudha P;
Visualization: Amudha P;
Investigation: Meenalochini M and Amudha P;
Supervision: Meenalochini M;
Validation: Amudha P;
Writing- Reviewing and Editing: Meenalochini M and Amudha P; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Amudha P for this research completion and support.
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Amudha P
Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India.
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Cite this article
Meenalochini M and Amudha P, “Capsule Network for Pioneering Disease Detection in Cauliflower Cultivation with Mobile Application Development”, Journal of Machine and Computing, vol.5, no.4, pp. 2196-2210, October 2025, doi: 10.53759/7669/jmc202505170.
