Abstract
Artificial intelligence has a significant impact on all sectors. It is revolutionizing agriculture by replacing traditional methods with more efficient techniques and helping the world improve. The primary purpose of the article is to develop a deep learning-based system to detect and classify plant diseases to save the crop and the time of farmers as well as to protect people from getting ill. Initially, the data was taken from the PlantVillage dataset in fifteen classes, such as tomato spider mates, pepper bell healthy, tomato late blight, pepper bell bacterial spot, tomato early blight, potato late blight, tomato bacterial spot, potato early blight, potato healthy, tomato septoria leaf spot, tomato leaf mold, tomato yellow leaf curl virus, tomato target spot, tomato mosaic virus, and tomato healthy. Later the data is pre-processed to remove the noisy signals, and the pixel values are added and removed to resize the image size using the dilation and erosion technique. For feature extraction, contour feature techniques have been used along with adaptive thresholding techniques to obtain the cropped image. For classification, ten deep learning models such as DenseNet201, DenseNet121, NasNetLarge, Xception, ResNet152V2, EfficientNetB5, EfficientNetB7, VGG19, and MobileNetV2 along with the hybrid model (EfficientNetB7 and ResNet152V2) have been applied. The models have been trained and later evaluated based on precision, recall, accuracy, F1-score, and loss. The performance of the models has also been assessed for different classes during the training and validation phases. While experimenting, it was found that DenseNet201 obtained the highest validation accuracy and loss by 98.67% and 0.04. For precision, recall, and F1 score, DenseNet201 again got the highest values of 0.98, 0.99, and 0.98, respectively.
Similar content being viewed by others
Data Availability
Not applicable.
Code Availability
Not applicable.
References
Chowdhury ME, Rahman T, Khandakar A, Ayari MA, Khan AU, Khan MS, Ali SHM (2021) Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering 3(2):294–312
Timmerman A et al (2018) Plant disease: pathogens and cycles. Institute of Agriculture and Natural Resources, Nebraska
Bhise N, Kathet S, Jaiswar S, Adgaonkar A (2020) Plant disease detection using machine learning. Int Res J Eng Technol (IRJET) 7(7):2924–2929
Poornappriya TS, Gopinath R (2022) Rice plant disease identification using artificial intelligence approaches. Int J Electric Eng Technol 11(10):392–402
Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Info Process Agric 4(1):41–49
Koul A, Koul A (2020) Semantic segmentation and contextual information based image scene interpretation: a review. 2020 3rd international conference on information and computer technologies (ICICT). IEEE, New York, pp 148–153
Dhiman B, Kumar Y, Kumar M (2022) Fruit quality evaluation using machine learning techniques: review, motivation and future perspectives. Multimed Tools Appl 81:1–23
Panchal AV, Patel SC, Bagyalakshmi K, Kumar P, Khan IR, Soni M (2021) Image-based plant diseases detection using deep learning. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.07.281
Goel N, Kaur S, Kumar Y (2022) Machine learning-based remote monitoring and predictive analytics system for crop and livestock. AI, edge and IoT-based smart agriculture. Academic Press, Cambridge, pp 395–407
Ramesh S, Hebbar R, Niveditha M, Pooja R, Shashank N, Vinod PV (2018) Plant disease detection using machine learning. 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C). IEEE, New York, pp 41–45
Chohan M, Khan A, Chohan R, Katpar SH, Mahar MS (2020) Plant disease detection using deep learning. Int J Recent Technol Eng 9(1):909–914
Praveen P, Nischitha M, Supriya C, Yogitha M, Suryanandh A (2022) To detect plant disease identification on leaf using machine learning algorithms. Intelligent system design. Springer, Singapore, pp 239–249
Javidan SM, Banakar A, Vakilian KA, Ampatzidis Y (2023) Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agric Technol 3:100081
Parthiban S, Moorthy S, Sabanayagam S, Shanmugasundaram S, Naganathan A, Annamalai M, Balasubramanian S (2023) Deep learning based recognition of plant diseases. Computer vision and machine intelligence paradigms for SDGs. Springer, Singapore, pp 83–93
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Venkataramanan A, Honakeri DKP, Agarwal P (2019) Plant disease detection and classification using deep neural networks. Int J Comput Sci Eng 11(9):40–46
Trivedi J, Shamnani Y, Gajjar R (2020) Plant leaf disease detection using machine learning. International conference on emerging technology trends in electronics communication and networking. Springer, Singapore, pp 267–276
Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80:103615
Ahmed K, Shahidi TR, Alam SMI, Momen S (2019) Rice leaf disease detection using machine learning techniques. 2019 international conference on sustainable technologies for industry 4.0 (STI). IEEE, New York, pp 1–5
Panigrahi KP, Das H, Sahoo AK, Moharana SC (2020) Maize leaf disease detection and classification using machine learning algorithms. Progress in computing, analytics and networking. Springer, Singapore, pp 659–669
Jumat MH, Nazmudeen MS, Wan AT (2018) Smart farm prototype for plant disease detection, diagnosis & treatment using IoT device in a greenhouse.
Mahum R, Munir H, Mughal ZUN, Awais M, Sher Khan F, Saqlain M, Tlili I (2023) A novel framework for potato leaf disease detection using an efficient deep learning model. Human Ecol Risk Assess Int J 29(2):303–326
Vallabhajosyula S, Sistla V, Kolli VKK (2022) Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot 129(3):545–558
Al-gaashani MS, Shang F, Muthanna MS, Khayyat M, Abd El-Latif AA (2022) Tomato leaf disease classification by exploiting transfer learning and feature concatenation. IET Image Proc 16(3):913–925
Subetha T, Khilar R, Christo MS (2021) A comparative analysis on plant pathology classification using deep learning architecture–Resnet and VGG19. Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.11.993
Bhumica D, Yogesh K, Singla I (2020) Fruit quality evaluation using different learning techniques. J Nat Remed 21(2):154–162
Yang G, He Y, Yang Y, Xu B (2020) Fine-grained image classification for crop disease based on attention mechanism. Front Plant Sci 11:600854
Verma D, Bordoloi D, Tripathi V (2021) Plant leaf disease detection using Mobilenetv2. Webology 18(5):3241–3246
Mehedi MHK, Hosain AS, Ahmed S, Promita ST, Muna RK, Hasan M, Reza MT (2022) Plant leaf disease detection using transfer learning and explainable AI. 2022 IEEE 13th annual information technology, electronics and mobile communication conference (IEMCON). IEEE, New York, pp 0166–0170
Moid MA, Chaurasia MA (2021) Transfer learning-based plant disease detection and diagnosis system using Xception. 2021 Fifth international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC). IEEE, New York, pp 1–5
Gao F, Sa J, Wang Z, Zhao Z (2021) Cassava disease detection method based on EfficientNet. 2021 7th international conference on systems and informatics (ICSAI). IEEE, New York, pp 1–6
Kaur S, Kumar Y, Koul A, Kumar Kamboj S (2022) A systematic review on metaheuristic optimization techniques for feature selections in disease diagnosis: open issues and challenges. Archiv Comput Methods Eng 2022:1–33
Koul A, Bawa RK, Kumar Y (2022) Artificial intelligence techniques to predict the airway disorders illness: a systematic review. Arch Comput Methods Eng 2022:1–34
Kumar Y, Koul A, Singla R et al (2022) Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03612-z
Goel N, Kaur S, Kumar Y (2022) Machine learning-based remote monitoring and predictive analytics system for crop and livestock. In: Abraham A, Dash S, Rodrigues JJPC, Acharya B, Pani SK (eds) AI, Edge, and IoT-based smart agriculture. Academic Press, Cambridge, pp 395–407
Kulkarni O (2018) Crop disease detection using deep learning. 2018 fourth international conference on computing communication control and automation (ICCUBEA). IEEE, New York, pp 1–4
Sibiya M, Sumbwanyambe M (2019) A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering 1(1):119–131
Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband.
Yamamoto K, Togami T, Yamaguchi N (2017) Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors 17(11):2557
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419
Dhiman B, Kumar Y, Hu YC (2021) A general purpose multi-fruit system for assessing the quality of fruits with the application of recurrent neural network. Soft Comput 25(14):9255–9272
Kaur P, Harnal S, Tiwari R, Upadhyay S, Bhatia S, Mashat A, Alabdali AM (2022) Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors 22:575
Trivedi NK (2021) Early detection and classification of apple leaf disease-using models. Sensors 08:1–12. https://doi.org/10.17605/OSF.IO/X8J6P
Funding
Not applicable.
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 (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
Kumar, Y., Singh, R., Moudgil, M.R. et al. A Systematic Review of Different Categories of Plant Disease Detection Using Deep Learning-Based Approaches. Arch Computat Methods Eng 30, 4757–4779 (2023). https://doi.org/10.1007/s11831-023-09958-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11831-023-09958-1