Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct 27;13(1):18475.
doi: 10.1038/s41598-023-45403-w.

Advanced deep learning techniques for early disease prediction in cauliflower plants

G Prabu Kanna  1 , S J K Jagadeesh Kumar  2 , Yogesh Kumar  3 , Ankur Changela  4 , Marcin Woźniak  5 , Jana Shafi  6 , Muhammad Fazal Ijaz  7
Affiliations

Advanced deep learning techniques for early disease prediction in cauliflower plants

G Prabu Kanna et al. Sci Rep. .

Erratum in

Abstract

Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers' incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Parts of cauliflower.
Figure 2
Figure 2
System design for detection and classification of cauliflower diseases.
Figure 3
Figure 3
Pre-processed images of cauliflower.
Figure 4
Figure 4
EDA of cauliflower images.
Figure 5
Figure 5
Feature extraction techniques on cauliflower images.
Figure 6
Figure 6
Architecture of Xception model.
Figure 7
Figure 7
Modules used in the architecture of all versions of EfficientNet model.
Figure 8
Figure 8
Architecture of MobileNetV2.
Figure 9
Figure 9
Architecture of DenseNet201.
Figure 10
Figure 10
Architecture of ResNet152V2.
Figure 11
Figure 11
Architecture of InceptionResNetV2.
Figure 12
Figure 12
Graphical analysis of models for detection of cauliflower diseases.
Figure 13
Figure 13
Confusion Matrix of models for detection of cauliflower diseases.
Figure 13
Figure 13
Confusion Matrix of models for detection of cauliflower diseases.
Figure 14
Figure 14
Performance evaluation of models for the classification of various diseases in cauliflower.
See this image and copyright information in PMC

References

    1. Yaseen AA, Ahmed SJ. Interaction effect of planting date and foliar application on some vegetative growth characters and yield of broccoli (Brassica olerasea var italica) grown under unheated plastic tunnel. J. Garmian Univ. 2017;4:405–418. doi: 10.24271/garmian.151. - DOI
    1. Rajbongshi, A., Sara, U. S., Shakil, R., Akter, B. & Uddin, M. S. VegNet: An extensive dataset of cauliflower images to recognize the diseases using machine learning and deep learning models. In Mendeley Data, V3. 10.17632/t5sssfgn2v.3 (2022).
    1. Abdull Razis AF, Noor NM. Cruciferous vegetables: Dietary phytochemicals for cancer prevention. Asian Pac. J. Cancer Prev. 2013;14(3):1565–1570. doi: 10.7314/APJCP.2013.14.3.1565. - DOI - PubMed
    1. Sharma SR, Singh PK, Chable V, Tripathi SK. A review of hybrid cauliflower development. J. New Seeds. 2004;6(2–3):151–193. doi: 10.1300/J153v06n02_08. - DOI
    1. Dan A, Jain R, Dwivedi RK, Kumar A. Evaluation of socio-economic conditions of cauliflower (Brassica oleracea) growers in Chaka block of Allahabad district Uttar Pradesh. J. Pharmacogn. Phytochem. 2020;9(5):148–151.