Abstract
Maize is ranked as the third most important food crop in India after rice and wheat. The cultivation of this particular crop and its associated agricultural practices have faced many challenges over the centuries. Maize is susceptible to Northern Leaf Blight (NLB), a highly contagious fungal foliar disease. The primary reason for the reduction in yield resulting from NLB is the loss of photosynthetic leaf area. Therefore, identifying diseases at an early stage is critical to ensure that the treatment process is carried out correctly and that the quality of results is maintained. This research uses a deep learning-based Attention U-Net model to explore a real-time, effective approach for segmentation and detecting NLB diseases in maize crops. Data augmentation is performed after image annotation to increase the model’s effectiveness, and the model is trained from scratch. The model was trained for a maximum of 50 epochs using an initial learning rate of 0.0001 with Adam as an optimizer, and its performance was tested on the test dataset. This study shows that the Attention U-Net model outperformed other image segmentation methods, such as Res U-Net and Plain U-Net, and showed better results with an Intersection over Union (IoU) of 72.41%, 70.91% and 51.95%, respectively. The proposed model achieves an average pixel-wise F1 score of 85.23%. The diseased segmentation accuracy clenched to 98.97%, and the Dice coefficient (DC) of disease spot segmentation is 81.39%. Adding an attention mechanism to the U-Net architecture improves its ability to express local features, resulting in improved NLB disease image segmentation performance.









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The data presented in this study can be freely and openly accessed via a repository on the Open Science Framework (https://osf.io/p67rz /).
References
Afzaal U, Bhattarai B, Pandeya YR, Lee J (2021) An instance segmentation model for strawberry diseases based on mask R-CNN. Sensors 21:6565. https://doi.org/10.3390/s21196565
Azath M, Zekiwos M, Bruck A (2021) Deep learning-based image processing for cotton leaf disease and pest diagnosis. J Electr Comput Eng 2021:1–10. https://doi.org/10.1155/2021/9981437
Bi C, Wang J, Duan Y et al (2020) MobileNet based apple leaf diseases identification. Mob Netw Appl 27:172–180. https://doi.org/10.1007/s11036-020-01640-1
Buslaev A, Seferbekov S, Iglovikov V, Shvets A (2018) Fully convolutional network for automatic road extraction from satellite imagery. In: IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 197–1973
Chaudhary P, Chaudhari AK, Cheeran AN, Godara S (2012) Color transform based approach for disease spot detection on plant leaf. Int J Comput Sci Telecommun 3:4–9
Chen J, Zhang D, Nanehkaran YA (2020) Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl 79:31497–31515. https://doi.org/10.1007/s11042-020-09669-w
Chen S, Zhang K, Zhao Y et al (2021) An approach for rice bacterial leaf streak disease segmentation and disease severity estimation. Agriculture 11:420. https://doi.org/10.3390/agriculture11050420
Chung CL, Jamann T, Longfellow J, Nelson R (2010) Characterization and fine-mapping of a resistance locus for northern leaf blight in maize bin 8.06. Theor Appl Genet 121:205–227. https://doi.org/10.1007/s00122-010-1303-z
Dande SC, Agrawal SS, Hirekhan SR (2016) Implementation of colour image steganography using LSB and edge detection technique: a LabVIEW approach. International Conference on Communication and Signal Processing, ICCSP 2016:1466–1470. https://doi.org/10.1109/ICCSP.2016.7754401
DeChant C, Wiesner-Hanks T, Chen S et al (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107:1426–1432. https://doi.org/10.1094/PHYTO-11-16-0417-R
Dhaka VS, Meena SV, Rani G et al (2021) A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors 21:4749. https://doi.org/10.3390/s21144749
Donatelli M, Magarey RD, Bregaglio S et al (2017) Modelling the impacts of pests and diseases on agricultural systems. Agric Syst 155:213–224. https://doi.org/10.1016/j.agsy.2017.01.019
Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on SVM classification method. Comput Electron Agric 137:52–58. https://doi.org/10.1016/j.compag.2017.03.016
Fekri-Ershad S (2020) Bark texture classification using improved local ternary patterns and multilayer neural network. Expert Syst Appl 158:113509. https://doi.org/10.1016/j.eswa.2020.113509
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318. https://doi.org/10.1016/j.compag.2018.01.009
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 (Switzerland) 17. https://doi.org/10.3390/s17092022
Gajjar R, Gajjar N, Thakor VJ et al (2021) Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis Comput 38:2923–2938. https://doi.org/10.1007/s00371-021-02164-9
Garcia J, Barbedo A (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96–107. https://doi.org/10.1016/j.biosystemseng.2019.02.002
Haque MA, Marwaha S, Deb CK et al (2022) Deep learning-based approach for identification of diseases of maize crop. Sci Rep 12:1–14. https://doi.org/10.1038/s41598-022-10140-z
Hassan SM, Maji AK, Jasiński M et al (2021) Identification of plant-leaf diseases using cnn and transfer-learning approach. Electronics (Switzerland) 10. https://doi.org/10.3390/electronics10121388
Hooda KS, Khokhar MK, Shekhar M et al (2017) Turcicum leaf blight—sustainable management of a re-emerging maize disease. J Plant Dis Prot 124:101–113. https://doi.org/10.1007/s41348-016-0054-8
Hooda KS, Khokhar MK, Parmar H et al (2017) Banded leaf and sheath blight of maize: historical perspectives, current status and future directions. Proc Natl Acad Sci India Sect B Biol Sci 87:1041–1052. https://doi.org/10.1007/s40011-015-0688-5
Huang M, Xu G, Li J, Huang J (2021) A method for segmenting disease lesions of maize leaves in real time using attention YOLACT++. Agriculture 11:1216. https://doi.org/10.3390/agriculture11121216
Hughes DP, Salathe M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv. https://doi.org/10.1111/1755-0998.12237
Jagtap SB, Hambarde SM (2014) Agricultural plant leaf disease detection and diagnosis using image processing based on morphological feature extraction. IOSR J VLSI Signal Proc 4:24–30
Jamann TM, Luo X, Morales L et al (2016) A remorin gene is implicated in quantitative disease resistance in maize. Theor Appl Genet 129:591–602. https://doi.org/10.1007/s00122-015-2650-6
Jasim MA, Al-Tuwaijari JM (2020) Plant leaf diseases detection and classification using image processing and deep learning techniques. In: Proceedings of the 2020 international conference on computer science and software engineering (CSASE). IEEE, pp 259–265
Jiang P, Chen Y, Liu B et al (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069–59080. https://doi.org/10.1109/ACCESS.2019.2914929
Julia S, Pangirayi T, John D et al (2013) Smallholder farmers perceptions of maize diseases, pests, and other production constraints, their implications for maize breeding and evaluation of local maize cultivars in KwaZulu-Natal, South Africa. Afr J Agric Res 8:1790–1798. https://doi.org/10.5897/ajar12.1906
Li Z, Chen P, Shuai L et al (2022) A copy paste and semantic segmentation-based approach for the classification and assessment of significant rice diseases. Plants 11:3174. https://doi.org/10.3390/plants11223174
Lu Y, Yi S, Zeng N et al (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384. https://doi.org/10.1016/j.neucom.2017.06.023
Lv M, Zhou G, He M et al (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
Ma J, Du K, Zheng F et al (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18–24. https://doi.org/10.1016/j.compag.2018.08.048
Mallowa SO, Esker PD, Paul PA et al (2015) Effect of maize hybrid and foliar fungicides on yield under low foliar disease severity conditions. Phytopathology 105:1080–1089. https://doi.org/10.1094/PHYTO-08-14-0210-R
Mitra M (1931) A comparative study of species and strains of Helminthosporium on certain indian cultivated crops. Trans Br Mycol Soc 15:254-IN2. https://doi.org/10.1016/s0007-1536(31)80014-2
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https://doi.org/10.3389/fpls.2016.01419
Oktay O, Schlemper J, Folgoc L Le et al (2018) Attention U-Net: learning where to look for the pancreas. 1st conference on medical imaging with deep learning
Patki SS, Sable GS (2016) Cotton leaf disease detection & classification using multi SVM. Int J Adv Res Comput Commun Eng 5:165–168. https://doi.org/10.17148/IJARCCE.2016.51034
Phadikar S (2012) Classification of rice leaf diseases based on morphological changes. Int J Inf Electron Eng 2:460–463. https://doi.org/10.7763/ijiee.2012.v2.137
Picon A, Alvarez-Gila A, Seitz M et al (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280–290. https://doi.org/10.1016/j.compag.2018.04.002
Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases 11:357–373. https://doi.org/10.3233/IDT-170301
Rai CK, Pahuja R (2022) Digital image processing-based virtual instruments for the detection and classification of Eaten leaves. J East China Univ Sci Technol 65:877–885. https://doi.org/10.5281/ZENODO.7081544
Rai CK, Pahuja R (2023) Classification of diseased cotton leaves and plants using improved deep convolutional neural network. Multimed Tools Appl 82:25307–25325. https://doi.org/10.1007/s11042-023-14933-w
Kumar Rai C, Pahuja R, Kumar Chabbra J (2021) Implementation of virtual instrumentation system for estimation of eaten leaf area using digital image processing. In: 2021 Sixth International Conference on Image Information Processing (ICIIP). IEEE, pp 472–476
Reddy TR, Reddy PN, Reddy RR (2013) TURCICUM LEAF BLIGHT OF MAIZE INCITED BY Exserohilum turcicum : A REVIEW. Int J Appl Biol Pharm Technol 5:54–59
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. IEEE Access 9:1–8. https://doi.org/10.1109/ACCESS.2021.3053408
Rothe PR, Kshirsagar RV (2015) Cotton leaf disease identification using pattern recognition techniques. In: 2015 International conference on pervasive computing (ICPC). IEEE, pp 1–6
Sagar A, Dheeba J (2020) On using transfer learning for plant disease detection. bioRxiv. https://doi.org/10.1101/2020.05.22.110957
Savary S, Willocquet L, Pethybridge SJ et al (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3:430–439. https://doi.org/10.1038/s41559-018-0793-y
Singh UP, Chouhan SS, Jain S, Jain S (2019) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7:43721–43729. https://doi.org/10.1109/ACCESS.2019.2907383
Sladojevic S, Arsenovic M, Anderla A et al (2016) Deep neural networks based recognition of plant diseases by leaf image classification 2016. https://doi.org/10.1155/2016/3289801
Springer (2019) Springer guidelines for authors of proceedings lecture notes. 1–11
Stewart EL, Wiesner-Hanks T, Kaczmar N et al (2019) Quantitative phenotyping of northern leaf blight in UAV images using deep learning. Remote Sens 11:1–10. https://doi.org/10.3390/rs11192209
Sun J, Yang Y, He X, Wu X (2020) Northern maize leaf blight detection under complex field environment based on deep learning. IEEE Access 8:33679–33688. https://doi.org/10.1109/ACCESS.2020.2973658
Thangaraj R, Anandamurugan S, Kaliappan VK (2021) Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J Plant Dis Prot 128:73–86. https://doi.org/10.1007/s41348-020-00403-0
Toda Y, Okura F (2019) How convolutional neural networks diagnose plant disease. Plant Phenomics 2019:1–14. https://doi.org/10.34133/2019/9237136
Tzounis A, Katsoulas N, Bartzanas T (2017) ScienceDirect Internet of Things in agriculture, recent advances and future challenges. Biosyst Eng 164:31–48. https://doi.org/10.1016/j.biosystemseng.2017.09.007
Vallabhajosyula S, Sistla V, Kolli VKK (2021) Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot 129:545–558. https://doi.org/10.1007/s41348-021-00465-8
Wang Z, Zhang S (2018) Segmentation of corn leaf disease based on fully convolution neural network. Acad J Comput Inf Sci 1: 9–18. https://doi.org/10.25236/ajcis.010002
Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017:1–8. https://doi.org/10.1155/2017/2917536
Wiesner-Hanks T, Stewart EL, Kaczmar N et al (2018) Image set for deep learning: field images of maize annotated with disease symptoms. BMC Res Notes 11:10–12. https://doi.org/10.1186/s13104-018-3548-6
Yuan M, Liu Z, Wang F (2019) Using the wide-range attention u-net for road segmentation. Remote Sens Lett 10:506–515. https://doi.org/10.1080/2150704X.2019.1574990
Zhang S, Zhang C (2023) Modified U-Net for plant diseased leaf image segmentation. Comput Electron Agric 204:107511. https://doi.org/10.1016/j.compag.2022.107511
Zhang X, Qiao YUE, Meng F et al (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30377. https://doi.org/10.1109/ACCESS.2018.2844405
Zhang Y, Song C, Zhang D (2020) Deep learning-based object detection improvement for tomato disease. IEEE Access 8:56607–56614. https://doi.org/10.1109/ACCESS.2020.2982456
Zhang J, Rao Y, Man C et al (2021) Identification of cucumber leaf diseases using deep learning and small sample size for agricultural internet of things. Int J Distrib Sens Netw 17. https://doi.org/10.1177/15501477211007407
Zhou G, Zhang W, Chen A et al (2019) Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE Access 7:143190–143206. https://doi.org/10.1109/ACCESS.2019.2943454
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Rai, C.K., Pahuja, R. Northern maize leaf blight disease detection and segmentation using deep convolution neural networks. Multimed Tools Appl 83, 19415–19432 (2024). https://doi.org/10.1007/s11042-023-16398-3
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DOI: https://doi.org/10.1007/s11042-023-16398-3