Abstract
The biggest contribution to the Indian economy is made through agriculture. It is one of the main sources of income for farmers. The major concern for the farmers during cultivation is to produce crops of good quality and quantity. For a wide range of productivity and protection from various diseases, several techniques must be revised. Machine learning techniques were utilized for the categorization of leaf diseases, because earlier methods were time-consuming and labor-intensive. Better results are achieved using machine learning techniques, along with certain limitations. Deep Convolutional Neural networks are now one of the finest methods for categorizing leaf diseases in crops. There are still issues with getting access to trustworthy models for identifying a variety of diseases and segmenting lesion areas for assessing severity in real-world field conditions, despite the growing popularity of deep learning approaches for detecting various diseases. Under Deep Convolutional Neural Networks, various architectures perform well for image classification. The quantitative evaluation of features that increase a plant’s ability to withstand disease is crucial in the selection of plant breeders. As a result, it is vital to take advantage of the disease-affected regions’ ability to determine how badly diseased the leaves are. To address these issues, CNN-based segmentation model was proposed to separate the wheat leaf diseases from the wheat leaf image dataset at the pixel level. This is superior to the segmentation algorithms currently in use. Our ultimate goal is to assist farmers in detecting and learning about early-stage illnesses in wheat leaves. To classify the disorders, a Deep Convolutional Neural Network was used. The investigation made use of a dataset including 4000 images of wheat leaves infected with three different types of leaf diseases: powdery mildew, leaf rust, and spot blotch. This manuscript includes the proposed method for feature extraction using the Point Rend deep segmentation model, followed by classification through the EfficientNet architecture. The results reveal that the proposed model is more accurate, showing a classification accuracy of 99.43% in comparison with classification performed on wheat leaf diseases without employing segmentation networks.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig10_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig12_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-023-02571-w/MediaObjects/42979_2023_2571_Fig13_HTML.png)
Similar content being viewed by others
References
Pak M, Kim S. A review of deep learning in image recognition. In: 2017 4th international conference on computer applications and information processing technology (CAIPT). IEEE; 2017. p. 1–3.
Saradhambal G, Dhivya R, Latha S, Rajesh R. Plant disease detection and its solution using image classification. Int J Pure Appl Math. 2018;119(14):879–84.
Kumar KV, Jayasankar T. An identification of crop disease using image segmentation. Int J Pharm Sci Res. 2019;10(3):1054–64.
Zala C, Patel P. A survey on applications of deep learning in agriculture. Int J Sci Res Rev. 2019;7(6):336–44. https://doi.org/10.1007/s10462-020-09825-6.
Kaur P, Harnal S, Gautam V, Singh MP, Singh SP. Comparative analysis of segmentation models to detect leaf diseases in tomato plant. https://doi.org/10.21203/rs.3.rs-1893425/v1
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.
Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. 2020;53:5455–516.
Yang X, Sun M. A survey on deep learning in crop planting. IOP Conf Ser Mater Sci Eng. 2019;490(6):062053. https://doi.org/10.1088/1757-899X/490/6/062053.
Zhu N, Liu X, Liu Z, Hu K, Wang Y, Tan J, Huang M, Zhu Q, Ji X, Jiang Y, Guo Y. Deep learning for smart agriculture: concepts, tools, applications, and opportunities. Int J Agric Biol Eng. 2018;11(4):32–44.
Altenberger F, Lenz C. A non-technical survey on deep convolutional neural network architectures. ar**v preprint ar**v:1803.02129. 2018; 1–17. https://doi.org/10.48550/ar**v.1803.02129
Arya S, Singh R. An analysis of deep learning techniques for plant leaf disease detection. Int J Computer Sci Inf Secur. 2019;17(7):73–80.
Deenan S, Janakiraman S, Nagachandrabose S. Image segmentation algorithms for Banana leaf disease diagnosis. J Inst Eng (India) Ser C. 2020;101:807–20. https://doi.org/10.1007/s40032-020-00592-5.
Singh V. Sunflower leaf diseases detection using image segmentation based on particle swarm optimization. Artif Intell Agric. 2019;3:62–8. https://doi.org/10.1016/j.aiia.2019.09.002.
Iqbal MA, Talukder KH. Detection of potato disease using image segmentation and machine learning. In: 2020 International Conerence on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE; 2020. p. 43–7.
Ashqar BA, Abu-Naser SS. Image-based tomato leaves diseases detection using deep learning. Int J Acad Eng Res. 2018;2(12):10–6.
Zhang S, Wang H, Huang W, You Z. Plant diseased leaf segmentation and recognition by fusion of superpixel. K-means PHOG Optik. 2018;157:866–72. https://doi.org/10.1016/j.ijleo.2017.11.190.
Jadhav SB, Udupi VR, Patil SB. Identification of plant diseases using convolutional neural networks. Int J Inf Technol. 2021;13(6):2461–70. https://doi.org/10.1007/s41870-020-00437-5.
Divyanth LG, Ahmad A, Saraswat D. A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery. Smart Agric Technol. 2023;3: 100108. https://doi.org/10.1016/j.atech.2022.100108.
Krishnan VG, Saradhi MV, Dhanalakshmi G, Somu CS, Theresa WG. Design of M3FCM based convolutional neural network for prediction of wheat disease. Int J Intell Syst Appl Eng. 2023;11(2s):203–10.
Chowdhury ME, Rahman T, Khandakar A, Ayari MA, Khan AU, Khan MS, Al-Emadi N, Reaz MB, Islam MT, Ali SH. Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering. 2021;3(2):294–312. https://doi.org/10.3390/agriengineering3020020.
Li Y, Qiao T, Leng W, Jiao W, Luo J, Lv Y, Tong Y, Mei X, Li H, Hu Q, Yao Q. Semantic segmentation of wheat stripe rust images using deep learning. Agronomy. 2022;12(12):2933. https://doi.org/10.3390/agronomy12122933.
Ngugi LC, Abdelwahab M, Abo-Zahhad M. Tomato leaf segmentation algorithms for mobile phone applications using deep learning. Comput Electron Agric. 2020;178: 105788. https://doi.org/10.1016/j.compag.2020.105788.
Ozguven MM, Adem K. Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A. 2019;535: 122537. https://doi.org/10.1016/j.physa.2019.122537.
Sunil CK, Jaidhar CD, Patil N. Cardamom plant disease detection approach using EfficientNetV2. IEEE Access. 2021;10:789–804. https://doi.org/10.1109/ACCESS.2021.3138920.
Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR; 2019. p. 6105–14.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proc. of the IEEE conference on computer vision and pattern recognition. 2018; 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
Kirillov A, Girshick R, He K, Dollár P. Panoptic feature pyramid networks. In: Proc. of the IEEE/CVF conference on computer vision and pattern recognition. 2019; 6399–6408. https://doi.org/10.48550/ar**v.1901.02446
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell. 2017;40(4):834–48. https://doi.org/10.1109/TPAMI.2017.2699184.
Cheng B, Collins MD, Zhu Y, Liu T, Huang TS, Adam H, Chen LC. Panoptic-deeplab. ar**v preprint ar**v:1910.04751. 2019. https://doi.org/10.48550/ar**v.1910.04751
ArcGIS API for Python In: How Mask R-CNN Works. https://developers.arcgis.com/python/guide/how-maskrcnn-works/. Accessed 30 July 2023
Shu JH, Nian FD, Yu MH, Li X. An improved mask R-CNN model for multiorgan segmentation. Math Probl Eng. 2020;2020:1–1. https://doi.org/10.1155/2020/8351725.
Wu X, Wen S, **e YA. Improvement of Mask-RCNN object segmentation algorithm. In: Intelligent Robotics and: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proc: Part I 12 2019. Springer International Publishing; 2019. p. 582–91.
Afzaal U, Bhattarai B, Pandeya YR, Lee J. An instance segmentation model for strawberry diseases based on mask R-CNN. Sensors. 2021;21(19):6565. https://doi.org/10.3390/s21196565.
Kirillov A, Wu Y, He K, Girshick R. Pointrend: Image segmentation as rendering. In: Proc. of the IEEE/CVF conference on computer vision and pattern recognition 2020; 9799–9808. https://doi.org/10.48550/ar**v.1912.08193
https://affine.ai/detectron2-fpn-pointrend-model-for-amazing-satellite-image-segmentation/. Accessed 12 June 2023
Wang P, Niu T, Mao Y, Zhang Z, Liu B, He D. Identification of apple leaf diseases by improved deep convolutional neural networks with an attention mechanism. Front Plant Sci. 2021;12: 723294. https://doi.org/10.3389/fpls.2021.723294.
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks-based recognition of plant diseases by leaf image classification. Comput Intell Neurosci. 2016;2016:1–11. https://doi.org/10.1155/2016/3289801.
Gieseke F, Bloemen S, van den Bogaard C, Heskes T, Kindler J, Scalzo RA, Ribeiro VA, van Roestel J, Groot PJ, Yuan F, Möller A. Convolutional neural networks for transient candidate vetting in large-scale surveys. Mon Not R Astron Soc. 2017;472(3):3101–14. https://doi.org/10.1093/mnras/stx2161.
Storey G, Meng Q, Li B. Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture. Sustainability. 2022;14(3):1458. https://doi.org/10.3390/su14031458.
Wallelign S, Polceanu M, Buche C. Soybean plant disease identification using convolutional neural network. In: FLAIRS conference 2018; 146–151.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sharma, T., Sethi, G.K. Improving Wheat Leaf Disease Image Classification with Point Rend Segmentation Technique. SN COMPUT. SCI. 5, 244 (2024). https://doi.org/10.1007/s42979-023-02571-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02571-w