Abstract
Deep Learning is acquiring momentum in the agricultural field for crop disease detection using image processing due to its computational power. Several deep learning techniques have been implemented in different domains and recently introduced in the field of agriculture to classify and predict the diseases of crops. Based on images of banana crops in the early stages of development, the objective of this research study is to create a prediction model using two types of Convolutional Neural Networks (CNN) architectures, namely, AlexNet and ResNet50. In order to carry out the empirical study, the PlantVillage dataset for the Banana plant with 510 images of banana leaves was used to train and test the networks. Results were analyzed using four parameters namely; training accuracy (TA), training loss (TL), validation accuracy (VA), and validation loss (VL). It was observed that ResNet50 outperformed the other one with better results at 88.54% when validation accuracy is considered as a performance evaluation measure. The results of this study will be useful for farmers as they can make timely interventions in the case of Banana Black Sigatoka (BBS) and Banana Bacterial Wilt (BBW) diseases.
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Acknowledgements
Authors are thankful to the Department of Science and Technology, Government of India, Delhi for funding a project on “Application of IoT in Agriculture Sector” through ICPS division. This work is a part of the project work.
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Sahu, P., Chug, A., Singh, A.P., Singh, D., Singh, R.P. (2021). Deep Learning Models for Crop Quality and Diseases Detection. In: Dave, M., Garg, R., Dua, M., Hussien, J. (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-7533-4_67
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DOI: https://doi.org/10.1007/978-981-15-7533-4_67
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