Version 1
: Received: 12 June 2024 / Approved: 14 June 2024 / Online: 14 June 2024 (08:22:52 CEST)
How to cite:
Sarawagi, K.; Dhiman, H.; Pagrotra, A.; Talwandi, N. S. Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases. Preprints2024, 2024060986. https://doi.org/10.20944/preprints202406.0986.v1
Sarawagi, K.; Dhiman, H.; Pagrotra, A.; Talwandi, N. S. Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases. Preprints 2024, 2024060986. https://doi.org/10.20944/preprints202406.0986.v1
Sarawagi, K.; Dhiman, H.; Pagrotra, A.; Talwandi, N. S. Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases. Preprints2024, 2024060986. https://doi.org/10.20944/preprints202406.0986.v1
APA Style
Sarawagi, K., Dhiman, H., Pagrotra, A., & Talwandi, N. S. (2024). Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases. Preprints. https://doi.org/10.20944/preprints202406.0986.v1
Chicago/Turabian Style
Sarawagi, K., Ashutosh Pagrotra and Navjot Singh Talwandi. 2024 "Deep Learning for Early Disease Detection: A CNN Approach to Classify Potato, Tomato, and Pepper Leaf Diseases" Preprints. https://doi.org/10.20944/preprints202406.0986.v1
Abstract
Early disease detection is crucial for maximizingcrop yields and minimizing financial losses in agriculture. Inorder to categorise leaf diseases in pepper, tomato, and potato plants, this study suggests a revolutionary CNN design. The algorithm collects disease-specific features from input photos by using data augmentation techniques to artificially enlarge the training dataset. ReLU-activated convolutional layers gradually capture features at different levels, from low to high, while max pooling layers minimise spatial dimensionality. After thecharacteristics are extracted, a fully-connected layers with a softmax activation function classify them into illness groups. A well chosen dataset containing labelled healthy and sick leaves is used to train and validate the model. Generalizability to untesteddata is ensured via performance evaluation on an independent testing set. The field of deep learning for agricultural applications benefits from this research. This strategy has the ability to rev- olutionise agricultural practices by enabling automated disease identification. This might result in early interventions, better crop health, and eventually sustainable agricultural production.
Keywords
convolutional neural network (CNN); data augmentation; plant disease detection; ReLU activation; deep learning; softmax activation
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
