Abstract
Deep learning (DL) has made significant progress in identifying and classifying plant diseases. The convolutional neural network (CNN) model was utilized to classify diseased and healthy tomato plant leaves for this study. Seven predominant DL models, namely LeNet 5, AlexNet, VGG19, Inception Net V3, ResNet50, DenseNet 121, and Efficient Net B0 have been used for tomato leaves disease classification. Deep feature extraction and fine-tuning strategies were utilized to adapt these DL models to the specific task of classification. The obtained features using deep feature extraction were then classified by fully connected layers of the CNNs. The experiments were carried out using the image data acquired from the Indian Agricultural Research Institute, India. The dataset consists of diseased and healthy tomato leaf images with a total count of 155 images. Data augmentation was used to increase the dataset size. Furthermore, three segmentation algorithms were also applied to remove the background and highlight the deep features. In this study, a comparison of the above-mentioned CNNs has been carried out to show the accuracy results achieved on the collected dataset. The evaluation results show that deep feature extraction with image segmentation techniques produced better results (up to 100% classification accuracy) than without segmentation. The outcome of this research will have a substantial impact on tomato disease prediction and early prevention.
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Acknowledgements
Authors are thankful to the Department of Science & Technology, Government of India, Delhi, for funding a project on “Application of IoT in Agriculture Sector” through the ICPS division. This work is a part of the project work.
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Sahu, P., Chug, A., Singh, A.P., Singh, D. (2023). TLDC: Tomato Leaf Disease Classification Using Deep Learning and Image Segmentation. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-19-2821-5_35
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DOI: https://doi.org/10.1007/978-981-19-2821-5_35
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