Abstract
The major cause of plant mortality and devastation, particularly among trees is plant diseases. This problem, however, may be handled and treated effectively through early detection. Crop/plant diseases must be identified and prevented as soon as possible to improve yield. Plant disease classification and identification by plant protection professionals through eye observation is a time-consuming and error-prone process. Previously, many researchers used hand-crafted techniques for the detection and classification of diseases. Also, due to a lack of knowledge experts and improper laboratory facilities, classification at an early stage is not possible. In terms of precision agriculture applications, developing effective, rapid, and highly successful computer-aided disease detection systems have become a necessity. A deep learning-based convolutional neural network (CNN) technique is used successfully in numerous computer vision tasks such as picture classification, object detection, segmentation, and image analysis for finding the best results. The existing models had been worked on a mixed crop approach and single class dataset with average accuracy. In this paper, the Modified InceptionResNet-V2 (MIR-V2) which is a form of the CNN model is used in conjunction with a transfer learning approach to recognize illnesses in images of tomato leaves. The proposed model is trained on a public dataset and self-collected dataset that includes seven different tomato leaf disease classifications with one healthy leaf. For the evaluation of model performance, different parameters such as dropout, learning rate, batch size, count of epochs, and accuracy are used. The disease classification accuracy rate for the applied models is 98.92% and the F1 score is 97.94%. The experimental results support the suggested approach’s validity and find it effective in detecting tomato leaf disease.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric 187:106279. https://doi.org/10.1016/j.compag.2021.106279
Ahmad J et al (2018) Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems. Comput Ind 98:23–33. https://doi.org/10.1016/j.compind.2018.02.005
Al-Asadi MA, Tasdemir S (2021) Empirical comparisons for combining balancing and feature selection strategies for characterizing football players using FIFA video game system. IEEE Access 9:149266–149286. https://doi.org/10.1109/ACCESS.2021.3124931
Ashwinkumar S, Rajagopal S, Manimaran V, Jegajothi B (2022) Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Mater Today Proc 51:480–487. https://doi.org/10.1016/j.matpr.2021.05.584
Bansal A (2020) Performance analysis of sign language detection using deep neural networks and computer vision. Alq J Med Appl Sci 3:78–81. https://doi.org/10.5281/zenodo.3951269
Barth R, IJsselmuiden J, Hemming J, Van Henten EJ (2019) Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation. Comput Electron Agric 161:291–304. https://doi.org/10.1016/j.compag.2017.11.040
Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020a) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393. https://doi.org/10.1016/j.compag.2020.105393
Chen J, Zhang D, Nanehkaran LD (2020b) Detection of rice plant diseases based on deep transfer learning. J Sci Food Agric 100:3246–3256. https://doi.org/10.1002/jsfa.10365
Chen SY, Lin C, Li GJ, Hsu YC, Liu KH (2021) Hybrid deep learning models with sparse enhancement technique for detection of newly grown tree leaves. Sensors 21:1–30. https://doi.org/10.3390/s21062077
Eli-Chukwu NC (2019) Applications of artificial intelligence in agriculture: a review. Eng Technol Appl Sci Res 9:4377–4383. https://doi.org/10.48084/etasr.2756
Gao J, Nuyttens D, Lootens P, He Y, Pieters JG (2018) Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosyst Eng 170:39–50. https://doi.org/10.1016/j.biosystemseng.2018.03.006
Gehlot M, Saini ML (2020) Analysis of different CNN architectures for tomato leaf disease classification. In: 2020 5th IEEE international conference on recent advances and innovations in engineering (ICRAIE), pp 1–6. https://doi.org/10.1109/ICRAIE51050.2020.9358279
Ghoushchi SJ, Ranjbarzadeh R, Najafabadi SA, Osgooei E, Tirkolaee EB (2021) An extended approach to the diagnosis of tumour location in breast cancer using deep learning. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03613-y
Greene D, Groenendyk M (2021) An environmental scan of virtual and augmented reality services in academic libraries. Libr Hi Tech 39:37–47. https://doi.org/10.1108/LHT-08-2019-0166
Gyamerah SA, Ngare P, Ikpe D (2019) Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function. https://arxiv.org/abs/1904.10959v2. https://doi.org/10.1016/j.ecolmodel.2014.01.030
Hemalatha NK et al (2022) Sugarcane leaf disease detection through deep learning. Deep Learn Sustain Agric. https://doi.org/10.1016/B978-0-323-85214-2.00003-3
Hughes DP, Salathe M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060
Kamath R, Balachandra M, Prabhu S (2019) Raspberry pi as visual sensor nodes in precision agriculture: a study. IEEE Access 7:45110–45122. https://doi.org/10.1109/ACCESS.2019.2908846
Kaur G (2020) Automated nutrient deficiency detection in plants: a review. PalArch’s J Archaeol Egypt Egyptol 17:5894–5901
Kaur P, Gautam V (2021) Plant biotic disease identification and classification based on leaf image: a review. In: Proceedings of 3rd international conference on computing informatics and networks (ICCIN), Springer Singapore, pp 597–610
Khanmohammadi E et al (2022) Development of dynamic balanced scorecard using case-based reasoning method and adaptive neuro-fuzzy inference system. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3140291
Korav S, Ram V, Ray LIP (2020) Critical period for crop-weed competition in groundnut (Arachis hypogaea L.) under mid altitude of Meghalaya. J Crop Weed 16(1):217–222
Le VNT, Ahderom S, Alameh K (2020) Performances of the LBP based algorithm over CNN models for detecting crops and weeds with similar morphologies. Sensors 20:2193. https://doi.org/10.3390/s20082193
Li J, Fine JP (2008) ROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies. Biostat 9:566–576. https://doi.org/10.1093/biostatistics/kxm050
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18:2674. https://doi.org/10.3390/s18082674
Liu Z, Gao J, Yang G, Zhang H, He Y (2016) Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci Rep 6:20410. https://doi.org/10.1038/srep20410
Maddikunta PKR et al (2021) Unmanned aerial vehicles in smart agriculture: applications, requirements, and challenges. IEEE Sens J 21:17608–17619. https://doi.org/10.1109/jsen.2021.3049471
Malathi V, Gopinath MP (2021) Classification of pest detection in paddy crop based on transfer learning approach. Agric Scand Sect B Soil Plant Sci 71:552–559. https://doi.org/10.1080/09064710.2021.1874045
Mishra AM, Gautam V (2021) Weed species identification in different crops using precision weed management: a review. In: Proceedings of CEUR workshop, pp 180–194
Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Comput Electron Agric 163:104859. https://doi.org/10.1016/j.compag.2019.104859
Pan S et al (2019) Diagnostic model of coronary microvascular disease combined with full convolution deep network with balanced cross-entropy cost function. IEEE Access 7:177997–178006. https://doi.org/10.1109/ACCESS.2019.2958825
Papapanou PN et al (2018) Periodontitis: consensus report of workgroup 2 of the 2017 world workshop on the classification of periodontal and peri-implant diseases and conditions. J Periodontol 89:173–182. https://doi.org/10.1002/JPER.17-0721
Patricio DI, Reider R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electron Agric 153:69–81. https://doi.org/10.1016/j.compag.2018.08.001
Pellas N, Kazanidis I, Konstantinou N, Georgiou G (2017) Exploring the educational potential of three-dimensional multi-user virtual worlds for STEM education: a mixed-method systematic literature review. Educ Inf Technol 22:2235–2279. https://doi.org/10.1007/s10639-016-9537-2
Polder G, Blok PM, Villiers HAC, van der Wolf JM, Kamp J (2019) Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Front Plant Sci 10:1–13. https://doi.org/10.3389/fpls.2019.00209
Qiang Z, He L, Dai F (2019) Identification of Plant leaf diseases based on Inception V3 transfer learning and fine-tuning. In: Smart city and informatization. iSCI 2019. Communications in computer and information science, vol 1122. Springer, Singapore, pp 118–127. https://doi.org/10.1007/978-981-15-1301-5_10
Raja R, Nguyen TT, Slaughter DC, Fennimore SA (2020) Real-time weed-crop classification and localisation technique for robotic weed control in lettuce. Biosyst Eng 192:257–274. https://doi.org/10.1016/j.biosystemseng.2020.02.002
Santa Cruz JF (2021) An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans. Intell Med 5:100027. https://doi.org/10.1016/j.ibmed.2021.100027
Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Nitrogen deficiency prediction of rice crop based on convolutional neural network. J Ambient Intell Humaniz Comput 11:5703–5711. https://doi.org/10.1007/s12652-020-01938-8
Singh RK, Tiwari A, Gupta RK (2022) Deep transfer modeling for classification of maize plant leaf disease. Multimed Tools Appl 81:6051–6067. https://doi.org/10.1007/s11042-021-11763-6
Storey G, Meng Q, Li B (2022) Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture. Sustainability 14:1458. https://doi.org/10.3390/su14031458
Suh HK, IJsselmuiden J, Hofstee JW, van Henten EJ (2018) Transfer learning for the classification of sugar beet and volunteer potato under field conditions. Biosyst Eng 174:50–65. https://doi.org/10.1016/j.biosystemseng.2018.06.017
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the thirty-first AAAI conference on artificial intelligence (AAAI-17), pp 4278–4284
Tran TT, Choi JW, Le TTH, Kim JW (2019) A comparative study of deep CNN in forecasting and classifying the macronutrient deficiencies on development of tomato plant. Appl Sci 9:1601. https://doi.org/10.3390/app9081601
Vallabhajosyula S, Sistla V, Kolli VKK (2022) 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
Xie Q, Luong MT, Hovy E, Le QV (2020) Self-training with noisy student improves ImageNet classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 10687–10698
Yasrab R, Zhang J, Smyth P, Pound MP (2021) Predicting plant growth from time-series data using deep learning. Remote Sens 13:331. https://doi.org/10.3390/rs13030331
Yin H, Gu YH, Park CJ, Park JH, Yoo SJ (2020) Transfer learning-based search model for hot pepper diseases and pests. Agriculture 10:439. https://doi.org/10.3390/agriculture10100439
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor 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
Kaur, P., Harnal, S., Gautam, V. et al. A novel transfer deep learning method for detection and classification of plant leaf disease. J Ambient Intell Human Comput 14, 12407–12424 (2023). https://doi.org/10.1007/s12652-022-04331-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-04331-9