Abstract
Automatic leaf disease detection is a challenging problem in smart agriculture due to the variations in appearance and the complicated backgrounds of plant diseases. Designing a deep convolutional neural network model that extracts visual illness features from the photos and then recognizes the diseases based on the retrieved features is a standard solution to this problem. This method works well when the background is simple, but it has poor accuracy and robustness when the background is complex. The primary factor in identifying leaf-based plant diseases is the color information of sick leaves. Most existing approaches for diagnosing plant diseases depend on the expert diagnosis, which inevitably results in field management and crop disease control behind the times. In this paper, our methodology is based on deep CNN which can be applied to solve these problems to improve the speed and accuracy of disease classification and recognition of plant diseases. We mainly focus on a deep CNN-based model with fine-tuning vegetable leaf diseases dataset. Transfer learning models implemented with famous pre-trained models such as VGG-16 and ResNet-50 are compared with our proposed model. Our proposed model outperforms various existing solutions with an Mean Average Precision (mAP) accuracy of 99.80%.
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
References
Bhattarai S (2018) New plant diseases dataset. https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
Fenu G, Malloci FM (2021) Diamos plant: a dataset for diagnosis and monitoring plant disease. Agronomy 11(11):2107
Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022
Ghazi MM, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228–235
Joshi RC, Kaushik M, Dutta MK, Srivastava A, Choudhary N (2021) Virleafnet: automatic analysis and viral disease diagnosis using deep-learning in vigna mungo plant. Ecolog Inf 61:101197
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recogn 71:1–13
Lv M, Zhou G, He M, Chen A, Zhang W, Hu Y (2020) Maize leaf disease identification based on feature enhancement and dms-robust alexnet. IEEE Access 8:57952–57966. https://doi.org/10.1109/ACCESS.2020.2982443
Mukti IZ, Biswas D (2019) Transfer learning based plant diseases detection using resnet50. In: 2019 4th International conference on electrical information and communication technology (EICT). IEEE, pp 1–6 (2019)
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N (2020) Plantdoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp 249–253 (2020)
Sinha A, Shekhawat RS (2020) Review of image processing approaches for detecting plant diseases. IET Image Proc 14(8):1427–1439
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosc (2016)
Su J, Liu C, Coombes M, Hu X, Wang C, Xu X, Li Q, Guo L, Chen WH (2018) Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Computers and electronics in agriculture 155:157–166
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9 (2015)
Tan M, Pang R, Le QV (2020) EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10781–10790 (2020)
Thuan D (2021) Evolution of yolo algorithm and yolov5: the state-of-the-art object detention algorithm
Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecolog Inf 63:101289
Yadav S, Sengar N, Singh A, Singh A, Dutta MK (2021) Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecolog Inf 61:101247
Zhang X, Qiao Y, Meng F, Fan C, Zhang M (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30377
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Reno, S., Turna, M.K., Tasfia, S., Abir, M., Aziz, A. (2023). Utilizing Deep Convolutional Neural Networks for Image-Based Plant Disease Detection. In: Suma, V., Lorenz, P., Baig, Z. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-99-1624-5_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-1624-5_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1623-8
Online ISBN: 978-981-99-1624-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)