ORIGINAL ARTICLE
Tomato fruit disease detection based on improved single shot detection algorithm
1
School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2023-05-17
Acceptance date: 2023-07-06
Online publication date: 2023-10-02
Corresponding author
Benedicta Nana Esi Nyarko
School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China
School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China
Journal of Plant Protection Research 2023;63(4):405-417
HIGHLIGHTS
- Applications of Artificial Intelligence Techniques in plant disease detection
- Impacts of plant disease on fruit security
- Improving Single Shot Object Detector (SSD) on tomato fruits disease detection
- Plant fruit feature extraction and Analysis
- Comparative Analysis of the proposed backbone to existing backbones
KEYWORDS
convolutional neural networkdeep learningfeature extractionmodel backboneplant disease detectionsingle shot detector algorithm
TOPICS
ABSTRACT
The tomato crop is more susceptible to disease than any other vegetable, and it can be
infected with over 200 diseases caused by different pathogens worldwide. Tomato plant
diseases have become a challenge to food security globally. Currently, diagnosing and preventing
tomato plant diseases is a challenge due to the lack of essential methods or tools.
The traditional techniques of detecting plant disease are arduous and error-prone. Utilizing
precise or automatic detection methods in spotting early plant disease can improve
the quality of food production and reduce adverse effects. Deep learning has significantly
increased the recognition accuracy of image classification and object detection systems in
recent years. In this study, a 15-layer convolutional neural network is proposed as the backbone
for single shot detector (SSD) to improve the detection of healthy, and three classes
of tomato fruit diseases. The proposed model performance is compared with ResNet-50,
AlexNet, VGG 16, and VGG19 as the backbone for Single shot detector. The findings of
the experiment showed that the proposed CNN-SDD achieved 98.87% higher detection
accuracy, which outperformed state-of-the-art models.
RESPONSIBLE EDITOR
Rafał Kukawka
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (45)
1.
Albattah W., Javed A., Nawaz M., Masood M., Albahli S. 2022. Artificial Intelligence-based drone system for multiclass plant disease detection using an improved efficient convolutional neural network. Frontiers in Plant Science 13: 1003152. DOI: https://doi.org/10.3389/fpls.2....
2.
Andrea Giovanni Caruso S. B., Giuseppe Parrella, Roberto Rizzo, Salvatore Davino, Stefano Panno. 2022. Tomato brown rugose fruit virus: a pathogen that is changing the tomato production worldwide. Annals of Applied Biology 181 (3): 258–274. DOI: https://doi.org/10.1111/aab.12....
3.
Ates C., Fidan H., Karacaoglu M., Dasgan H. 2019. The identification of the resistance levels of Fusarium oxysporum f. sp. radicis-lycopersici and tomato yellow leaf curl viruses in different tomato genotypes with traditional and molecular methods. Applied Ecology and Environmental Research 17 (2): 2203–2218. DOI: http://dx.doi.org/10.15666/aee....
4.
Bhujel A., Kim N.-E., Arulmozhi E., Basak J., Kim H.-T. 2022. A lightweight attention-based convolutional neural networks for tomato leaf disease classification. Mdpi- Agriculture 12 (2): 228. DOI: https://doi.org/10.3390/agricu....
5.
Bouni M., Hssina B., Douzi K., Douzi S. 2023. Impact of pretrained deep neural networks for tomato leaf disease prediction. Journal of Electrical and Computer Engineering 2023 (1): 1–11. DOI: https://doi.org/10.1155/2023/5....
6.
Durmuş H., Güneş E. O., Kırcı M. 2017. Disease detection on the leaves of the tomato plants by using deep learning. p. 1–5. In: Proceedings of "6th International Conference on Agro-Geoinformatics". 7–10 August 2017, Fairfax, VA, USA. DOI: https://doi.org/10.1109/Agro-G....
7.
Gaba S., Budhiraja I., Kumar V., Garg S., Kaddoum G., Hassan M. M. 2022. A federated calibration scheme for convolutional neural networks: models, applications and challenges. Computer Communications 192: 144–162. DOI: https://doi.org/10.1016/j.comc....
8.
Gatahi D. 2020. Challenges and opportunities in tomato production chain and sustainable standards introduction. International Journal of Horticulture Science and Technology 7 (3): 235–262. DOI: https://doi.org/10.22059/ijhst....
9.
Ghazal T. 2022. Convolutional neural network based intelligent handwritten document recognition. Computers, Materials & Continua 70 (3): 4563–4581. DOI: https://doi.org/10.32604/cmc.2....
10.
Golan K., Kot I., Kmieć K., Górska-Drabik E. 2023. Approaches to integrated pest management in Orchards: Comstockaspis perniciosa (comstock) case study. Mdpi- Agriculture 13 (1): 131. DOI: https://doi.org/10.3390/agricu....
11.
Guravaiah K., Bhavadeesh Y. S., Shwejan P., Vardhan A. H., Lavanya S. 2023. Third eye: object recognition and speech generation for visually impaired. Procedia Computer Science 218: 1144–1155. DOI: https://doi.org/10.1016/j.proc....
12.
Haar L. V., Elvira T., Ochoa O. 2023. An analysis of explainability methods for convolutional neural networks. Engineering Applications of Artificial Intelligence 117: 105606. DOI: https://doi.org/10.1016/j.enga....
13.
Hemathilake D., Gunathilake D. 2022. Agricultural productivity and food supply to meet increased demands. Future Foods 2022: 539–553. DOI: https://doi.org/10.1016/B978-0....
14.
Hofman-Bergholm M. 2023. A transition towards a food and agricultural system that includes both food security and planetary health. Mdpi-Foods 12 (1): 12. DOI: https://doi.org/10.3390/foods1....
15.
Humbal A., Pathak B. 2023. Application of nanotechnology in plant growth and diseases management: tool for sustainable agriculture. p. 145–168. In: "Agricultural and Environmental Nanotechnology" (F-L. Fabian, J.K. Patra, eds.). Springer, Singapore, 674 pp. DOI: https://doi.org/10.1007/978-98....
16.
Iqbal N., Mumtaz R., Shafi U., Zaidi S. M. H. 2021. Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms. PeerJ Computer Science 7 (8): e536. DOI: https://doi.org/10.7717/peerj-....
17.
J. A., Eunice J., Popescu D. E., Chowdary M. K., Hemanth J. 2022. Deep learning-based leaf disease detection in crops using images for agricultural applications. Agronomy 12 (10): 2395. DOI: https://doi.org/10.3390/agrono....
18.
Ji Y., Liu S., Hao Y. 2023. Realization of convolutional neural network based on FPGA. p. 761–765. In: Proceedings of "Third International Conference on Computer Vision and Data Mining (ICCVDM 2022). Hulun Buir, China. DOI: https://doi.org/10.1117/12.266....
19.
Khan H. R., Gillani Z., Jamal M. H., Athar A., Chaudhry M. T., Chao H., He Y., Chen M. 2023. Early identification of crop type for smallholder farming systems using deep learning on time-series sentinel-2 imagery. Mdpi-Sensors 23 (4): 1779. DOI: https://doi.org/10.3390/s23041....
20.
Knapp S., Peralta I. E. 2016. The tomato (Solanum lycopersicum L., Solanaceae) and its botanical relatives. p. 7–21. In "The Tomato Genome" (M. Causse, J. Giovannoni, M. Bouzayen, Z. Mohamed, eds.). Springer Berlin, Heidelberg, 259 pp. DOI: https://doi.org/10.1007/978-3-....
21.
Kremneva O., Danilov R., Gasiyan K., Ponomarev A. 2023. Spore-trapping device: an efficient tool to manage fungal diseases in winter wheat crops. Plants 12 (2): 391. DOI: https://doi.org/10.3390/plants....
22.
Leite G. L. D., Fialho A. 2018. Sustainable management of arthropod pests of tomato. p. 305–311. In: "Sustainable Management of Arthropod Pests of Tomato" (W. Wakil, G.E. Brust, T.M. Perring, eds.). Academic Press. DOI: https://doi.org/10.1016/B978-0....
23.
Li W., Zhang H., Wang G., Xiong G., Zhao M., Li G., Li R. 2023. Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robotics and Computer-Integrated Manufacturing 80: 102470. DOI: https://doi.org/10.1016/j.rcim....
24.
Liu W., Liu K., Chen D., Zhang Z., Li B., El-Mogy M. M., Tian S., Chen T. 2022. Solanum lycopersicum, a model plant for the studies in developmental biology, stress biology and food science. Foods 11 (16): 2402. DOI: https://doi.org/10.3390/foods1....
25.
Liu B., Luo L., Wang J., Lu Q., Wei H., Zhang Y., Zhu W. 2023a. An improved lightweight network based on deep learning for grape recognition in unstructured environments. Information Processing in Agriculture 2023. DOI: https://doi.org/10.1016/j.inpa... (in press).
26.
Liu H., Wang D., Xu K., Zhou P., Zhou D. 2023b. Lightweight convolutional neural network for counting densely piled steel bars. Automation in Construction 146: 104692. DOI: https://doi.org/10.1016/j.autc....
27.
Ma X., Man Q., Yang X., Dong P., Yang Z., Wu J., Liu C. 2023. Urban feature extraction within a complex urban area with an improved 3D-CNN using airborne hyperspectral data. Remote Sensing 15 (4): 992. DOI: https://doi.org/10.3390/rs1504....
28.
Mohan A., Krishnan R., Arshinder K., Vandore J., Ramanathan U. 2023. Management of postharvest losses and wastages in the Indian tomato supply chain – a temperature-controlled storage perspective. Mdpi-Sustainability 15 (2): 1331. DOI: https://doi.org/10.3390/su1502....
29.
Nazari K., Ebadi M. J., Berahmand K. 2022. Diagnosis of alternaria disease and leafminer pest on tomato leaves using image processing techniques. Journal of the Science of Food and Agriculture 102 (15): 6907–6920. DOI: https://doi.org/10.1002/jsfa.1....
30.
Nkongho R. N., Ndam L. M., Akoneh N. N., Tongwa Q. M., Njilar R. M., Agbor D. T., Sama V., Ojongakpa O. T., Ngone A. M. 2023. Vegetative propagation of F1 tomato hybrid (Solanum lycopersicum L.) using different rooting media and stem-nodal cuttings. Journal of Agriculture and Food Research 11: 100470. DOI: https://doi.org/10.1016/j.jafr....
31.
Nyarko B. N. E., Bin W., Zhou J., Agordzo G. K., Odoom J., Koukoyi E. 2022. Comparative analysis of AlexNet, Resnet-50, and Inception-V3 models on masked face recognition. p. 337–343. In: Proceedings of "2022 IEEE World AI IoT Congress (AIIoT)". 6–9 June 2022. DOI: https://doi.org/10.1109/AIIoT5....
32.
Ouhami M., Hafiane A., Es-Saady Y., El Hajji M., Canals R. 2021. Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Remote Sensing 13 (13): 2486. DOI: https://doi.org/10.3390/rs1313....
33.
Ozbay N., Newman S.E. 2004. Fusarium crown and root rot of tomato and control methods. Plant Pathology Journal 3 (1): 9–18. DOI: https://doi.org/10.3923/ppj.20....
34.
Özbay N., Newman S., Brown W. 2004. Evaluation of Trichoderma harzianum strains to control crown and root rot of greenhouse fresh market tomatoes. Acta Horticulturae 635: 635. DOI: https://doi.org/10.17660/ActaH....
35.
Peritore-Galve C., Tancos M., Smart C. 2020. Bacterial canker of tomato: revisiting a global and economically damaging seedborne pathogen. Plant Disease 105 (6): 1581–1595. DOI: https://doi.org/10.1094/PDIS-0....
36.
Picon A., Seitz M., Alvarez-Gila A., Mohnke P., Ortiz-Barredo A., Echazarra J. 2019. Crop conditional convolutional neural networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Computers and Electronics in Agriculture 167: 105093. DOI: https://doi.org/10.1016/j.comp....
37.
Rashid I., Shoala T. 2020. Nanoactivities of natural nanomaterials rosmarinic acid, glycyrrhizic acid and glycyrrhizic acid ammonium salt against tomato phytopathogenic fungi Alternaria alternata and Penicillium digitatum. Journal of Plant Protection Research 60 (2): 1–11. DOI: https://doi.org/10.24425/jppr.....
38.
Sánchez P., Vélez-del-Burgo A., Suñén E., Martínez J., Postigo I. 2022. Fungal allergen and mold allergy diagnosis: Role and relevance of Alternaria alternata Alt a 1 protein family. Journal of Fungi 8 (3): 277. DOI: https://doi.org/10.3390/jof803....
39.
Shi M., He P., Shi Y. 2022. Detecting extratropical cyclones of the northern hemisphere with single shot detector. Mdpi-Remote Sensing 14 (2): 254. DOI: https://doi.org/10.3390/rs1402....
40.
Sreedevi A., Manike C. 2023. Development of weighted ensemble transfer learning for tomato leaf disease classification solving low resolution problems. The Imaging Science Journal 71 (2): 1–27. DOI: https://doi.org/10.1080/136821....
41.
Thakur P. S., Sheorey T., Ojha A. 2023. VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimedia Tools and Applications 82 (1): 497–520. DOI: https://doi.org/10.1007/s11042....
42.
Thakur R., Mohanty S., Sethy P. K., Patro N., Sethy P., Acharya A. A. 2023. Detection of tomato leaf ailment using convolutional neural network technique. p. 193–202. In: Proceedings of "Third Mobile and Radio Communications and 5G Networks. 10–12 June, 2022. Krukshatra, India. DOI: https://doi.org/10.1007/978-98....
43.
Vig S., Arora A., Arya G. 2023. Automated license plate detection and recognition using deep learning. p. 419–431 In: Proceedings of "Advancements in Interdisciplinary Research: First International Conference, AIR 2022". 6–7 May 2022, Prayagraj, India. DOI: https://doi.org/10.1007/978-3-....
44.
Vishnoi V. K., Kumar K., Kumar B. 2021. Plant disease detection using computational intelligence and image processing. Journal of Plant Diseases and Protection 128: 19–53. DOI: https://doi.org/10.1007/s11119....
45.
Wei D., Wei X., Tang Q., Jia L., Yin X., Ji Y. 2023. RTLSeg: A novel multi-component inspection network for railway track line based on instance segmentation. Engineering Applications of Artificial Intelligence 119: 105822. DOI: 10.1016/j.engappai.2023.105822.
| eISSN: | 1899-007X |
| ISSN: | 1427-4345 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
