Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Journal article


Entuni, Chyntia Jaby, Afendi Zulcaffle, Tengku Mohd, Kipli, Kuryati and Kurugollu, Fatih 2020. Severity Estimation of Plant Leaf Diseases Using Segmentation Method. Applied Science and Engineering Progress. 14 (1), pp. 108-119. https://doi.org/10.14416/j.asep.2020.11.004
AuthorsEntuni, Chyntia Jaby, Afendi Zulcaffle, Tengku Mohd, Kipli, Kuryati and Kurugollu, Fatih
Abstract

Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment
for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray
spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate
the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the
Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr
to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and
compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation
algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy
C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average
performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This
algorithm is more effective than other algorithms in terms of not only better segmentation performance but also
low time complexity that is 34.75s in average with 0.2697s standard deviation.

KeywordsCorn; Fuzzy C-Means; K-Means; Otsu’s; Plant leaf disease detection
Year2020
JournalApplied Science and Engineering Progress
Journal citation14 (1), pp. 108-119
ISSN26729156
26730421
Digital Object Identifier (DOI)https://doi.org/10.14416/j.asep.2020.11.004
Web address (URL)http://hdl.handle.net/10545/625712
hdl:10545/625712
Publication dates09 Nov 2020
Publication process dates
Deposited15 Apr 2021, 15:12
Accepted21 Sep 2020
ContributorsUniversiti Malaysia Sarawak, Malaysia and University of Derby
File
File Access Level
Open
File
File Access Level
Open
Permalink -

https://repository.derby.ac.uk/item/94806/severity-estimation-of-plant-leaf-diseases-using-segmentation-method

Download files

  • 36
    total views
  • 139
    total downloads
  • 0
    views this month
  • 1
    downloads this month

Export as

Related outputs

Deep labeller: automatic bounding box generation for synthetic violence detection datasets
Nadeem, M., Kurugollu, F., Saravi, S., Atlam, H. and Franqueira, V. 2023. Deep labeller: automatic bounding box generation for synthetic violence detection datasets. Multimedia Tools and Applications. pp. 1-18. https://doi.org/10.1007/s11042-023-15621-5
Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image
Tengku Mohd Afendi, Zulcaffle,, Kurugollu, F., Kuryati, K., Joseph, A. and Bong, D. L. 2023. Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image. International Journal of Computing and Digital Systems. 2, pp. 1-10. https://doi.org/10.12785/ijcds/XXXXXX
Explaining deep neural networks: A survey on the global interpretation methods
Saleem, R., Yuan, B., Kurugollu, F., Anjum, A. and Liu, L. 2022. Explaining deep neural networks: A survey on the global interpretation methods. Neurocomputing. 513, pp. 165-180. https://doi.org/10.1016/j.neucom.2022.09.129
Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks
Saleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq 2020. Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks. IEEE. https://doi.org/10.1109/ucc48980.2020.00070
NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles
Ahmad, F., Kurugollu, Fatih, Kerrache, Chaker Abdelaziz, Sezer, Sakir and Liu, Lu 2021. NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3049189
A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices
Varan, Metin, Akgul, Akif, Kurugollu, Fatih, Sansli, Ahmet and Smith, K. 2021. A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices. Complexity. 2021, pp. 1-15. https://doi.org/10.1155/2021/2798534
Persation: an IoT based personal safety prediction model aided solution
Alofe, Olasunkanmi Matthew, Fatema, Kaniz, Azad, Muhammad Ajmal and Kurugollu, Fatih 2020. Persation: an IoT based personal safety prediction model aided solution. International Journal of Computing and Digital Systems.
MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles
Ahmad, F., Kurugollu, Fatih, Adnane, Asma, Hussain, Rasheed and Hussain, Fatima 2020. MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles. IEEE Internet of Things. https://doi.org/10.1109/JIOT.2020.2967568
CRT-BIoV: A cognitive radio technique for blockchain-enabled internet of vehicles
Rathee, Geetanjali, Ahmad, F., Kurugollu, Fatih, Azad, Muhammad, Iqbal, Razi and Imran, Muhammad 2020. CRT-BIoV: A cognitive radio technique for blockchain-enabled internet of vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2020.3004718
Vehicular sensor networks: Applications, advances and challenges
Kurugollu, Fatih, Ahmed, Syed Hassan, Hussain, Rasheed, Ahmad, F. and Kerrache, Chaker Abdelaziz 2020. Vehicular sensor networks: Applications, advances and challenges. Sensors. https://doi.org/10.3390/s20133686
Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes
Tasdemir, Kasim, Kurugollu, Fatih and Sezer, Sakir 2016. Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2016.2567073
Cascaded multimodal biometric recognition framework
Albesher, Badr, Kurugollu, Fatih, Bouridane, Ahmed and Baig, Asim 2013. Cascaded multimodal biometric recognition framework. IET Biometrics. https://doi.org/10.1049/iet-bmt.2012.0043
Privacy region protection for H.264/AVC with enhanced scrambling effect and a low bitrate overhead
Wang, Yongsheng, O׳Neill, Máire, Kurugollu, Fatih and O׳Sullivan, Elizabeth 2015. Privacy region protection for H.264/AVC with enhanced scrambling effect and a low bitrate overhead. Signal Processing: Image Communication. https://doi.org/10.1016/j.image.2015.04.013
Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory
Ahmaderaghi, Baharak, Kurugollu, Fatih, Rincon, Jesus Martinez Del and Bouridane, Ahmed 2018. Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/TCI.2018.2794065
Frontal view gait recognition with fusion of depth features from a time of flight camera
Afendi Tengku Mohd, Kurugollu, Fatih, Crookes, Danny, Bouridane, Ahmed and Farid, Mohsen 2018. Frontal view gait recognition with fusion of depth features from a time of flight camera. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2018.2870594
Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure
Barka, Ezedin, Kerrache, Chaker Abdelaziz, Benkraouda, Hadjer, Shuaib, Khaled, Ahmad, F. and Kurugollu, Fatih 2019. Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.3706
A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks
Ahmad, F., Adnane, Asma, Hussain, Rasheed and Kurugollu, Fatih 2019. A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks. IEEE.
A survey of deep learning solutions for multimedia visual content analysis.
Nadeem, Muhammad Shahroz, Franqueira, Virginia N. L., Zhai, Xiaojun and Kurugollu, Fatih 2019. A survey of deep learning solutions for multimedia visual content analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2019.DOI
Realization of blockchain in named data networking-based internet-of-vehicles
Ahmad, F., Kerrache, Chaker Abdelaziz, Kurugollu, Fatih and Hussain, Rasheed 2019. Realization of blockchain in named data networking-based internet-of-vehicles. IT Professional. https://doi.org/10.1109/MITP.2019.2912142
Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes.
Tasdemir, Kasim, Kurugollu, Fatih and Sezer, Sakir 2016. Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2016.2567073
Man-In-The-Middle attacks in Vehicular Ad-Hoc Networks: Evaluating the impact of attackers’ strategies.
Ahmad, F., Adnane, Asma, Franqueira, Virginia N. L., Kurugollu, Fatih and Liu, Lu 2018. Man-In-The-Middle attacks in Vehicular Ad-Hoc Networks: Evaluating the impact of attackers’ strategies. Sensors. 18 (11), p. 4040. https://doi.org/10.3390/s18114040
Digital video source identification based on green-channel photo response non-uniformity (G-PRNU)
Al-Athamneh, Mohammad, Kurugollu, Fatih, Crookes, Danny and Farid, Mohsen 2016. Digital video source identification based on green-channel photo response non-uniformity (G-PRNU). https://doi.org/10.5121/csit.2016.61105