Abstract
Crop disease is one of the major issues in agricultural fields. It reduces the food production process and produces a huge economic loss for farmers in agricultural lands. Deep learning models are built for detecting and identifying diseases at an early stage to help the farmers and provide food security. In this paper, transfer learning approach is used to identify and classify the diseased portion of leaves in farming lands. The benchmark dataset was taken from the Internet (Kaggle) for bell pepper leaf for identifying the diseases such as pepper leaf spot, mosaic virus, blight, damping off disease, verticillium wilt, blossom end rot, yellowing leaves, curling leaves, and healthy leaves. Ten pre-trained models are built for recognizing diseases and compared to obtain better accuracy. Adam optimizer and binary cross-entropy is used in the model for calculating loss function. In this paper, experimental results are presented on bell pepper diseases for MobileNetV2 with better accuracy of 99.42%. The various performance metrics such as accuracy, precision, recall and F1-score, ROC curve are used to determine the accuracy of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alruwaili M, Alanazi S, Abd El-Ghany S, Shehab A (2019) An efficient deep learning model for olive diseases detection. Int J Adv Comput Sci Appl Sci Inf (SAI) Organization Limited
Arinichev IV et al (2021) Rice fungal diseases recognition using modern computer vision techniques. Int J Fuzzy Logic Intell Syst 21(1):1–11
Atila Ü et al (2021) Plant leaf disease classification using EfficientNet deep learning model. Eco Inform 61:101182
Espejo-Garcia B et al (2022) Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies. Comput Electron Agric 196:106868
Gu YH, Yin H, Jin D, Zheng R, Yoo SJ (2022) Improved multi-plant disease recognition method using deep convolutional neural networks in six diseases of apples and pears. Agriculture 12(2):300
Hanh BT,Van Manh H, Nguyen N-V (2022) Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification. J Plant Dis Prot 129(3):623–634
Keh SS (2020) Semi-supervised noisy student pre-training on efficient net architectures for plant pathology classification. arXiv preprint arXiv:2012.00332
Math RKM, Dharwadkar NV (2022) Early detection and identification of grape diseases using convolutional neural networks. J Plant Dis Prot
Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants
Sanga S, Mero V, Machuve D, Mwanganda D (2020) Mobile-based deep learning models for banana diseases detection. arXiv preprint arXiv:2004.03718
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sowmiya, K., Thenmozhi, M. (2024). Metrics Evaluation of Bell Pepper Disease Classification Using Deep Convolutional Neural Network (DCNN). In: Mehta, G., Wickramasinghe, N., Kakkar, D. (eds) Innovations in VLSI, Signal Processing and Computational Technologies. WREC 2023. Lecture Notes in Electrical Engineering, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-99-7077-3_11
Download citation
DOI: https://doi.org/10.1007/978-981-99-7077-3_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7076-6
Online ISBN: 978-981-99-7077-3
eBook Packages: EngineeringEngineering (R0)