Automated Classification of Blackgram Plant Diseases Using ResNet-50: A Focus on Cuscuta Detection
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 10
Abstract
The identification and classification of plant diseases are crucial for ensuring the health and productivity of crops like blackgram (Vigna mungo), a widely cultivated legume. Among various threats, parasitic plants like Cuscuta (dodder) pose significant challenges, leading to severe yield losses. Traditional manual disease detection methods are time-consuming and prone to human error, highlighting the need for automated, accurate solutions. In this study, we propose a deep learning-based approach utilizing the ResNet-50 architecture to automatically classify diseases affecting blackgram plants, with a special focus on detecting Cuscuta infestations. ResNet-50, a robust convolutional neural network (CNN), is employed due to its ability to handle complex image recognition tasks while maintaining high accuracy. A dataset of blackgram plant images, including healthy plants and those affected by Cuscuta, was curated for training and validation. The model was trained using labeled images, achieving high classification accuracy through transfer learning and fine-tuning techniques. Data augmentation was employed to increase the dataset's diversity and improve model generalization. Our results demonstrate that the ResNet-50 model can effectively distinguish between healthy plants and those infested by Cuscuta, with an accuracy exceeding 98%. This automated system offers a scalable, efficient solution for early detection, enabling timely intervention and minimizing crop damage. Future work will focus on expanding the model's scope to identify other diseases and improving its real-time deployment capabilities in agricultural settings.
Authors and Affiliations
Nadakuditi Swarna Jyothi and Raavi Satya Prasad
Keywords
Related Articles
- EP ID EP752206
- DOI https://doi.org/10.46501/IJMTST1010002
- Views 37
- Downloads 0
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