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A disease monitoring system using multi-class capsule network for agricultural enhancement in muskmelon

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Abstract

For any agricultural society, the well-being of the plants is crucial to achieve a greater yield. The health and vigor of plants play a pivotal role in shaping the ultimate outcome of crop production. There are, too many infections affecting the plants generate harm to diverse economies and communities. It can also result in significant environmental losses. To prevent such losses, it is easier to diagnose diseases correctly and promptly at an early stage of plant life. This research mainly focuses on Muskmelon leaf diseases. Muskmelon is a remunerative crop with a short life span of around 65 days. Any disease attack in this duration will affect the crop entirely which in turn leads to yield loss. Hence, there needs an early prediction system for predicting diseases. The primary goal of this research is to develop a prediction model based of Multi Class Capsule Network for early detection of disease and pest in plants. The performance indicators examined for classification of leaf diseases are Accuracy, Precision, Recall, F1 score and, Loss function. The performance of Multi – Class Capsule Network [MCCN] is compared with existing pre-trained models such as, AlexNet, ResNet, VGG16, VGG19, GoogleNet, and CapsuleNet. Experimental results indicated that the MCCN model performs with an accuracy of 97.30% which is better than the accuracy of other models under considerations.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Authors and Affiliations

  1. School of Computing, SRM Institute of Science and Technology, Chennai, India

    K. Deeba & Amutha Balakrishnan

  2. School of Computer Science, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, United Arab Emirates

    Manoj Kumar

  3. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Manoj Kumar

  4. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Kadiyala Ramana & Gaurav Dhiman

  5. Department of Artificial Intelligence and Data Science, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

    Kadiyala Ramana

  6. Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

    C. Venkata Narasimhulu

  7. Centre of Research Impact and Outreach, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Gaurav Dhiman

  8. Division of Research and Development, Lovely Professional University, Phagwara, India

    Gaurav Dhiman

  9. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

    Gaurav Dhiman

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  1. K. Deeba
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Correspondence to Kadiyala Ramana.

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Deeba, K., Balakrishnan, A., Kumar, M. et al. A disease monitoring system using multi-class capsule network for agricultural enhancement in muskmelon. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18717-8

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