Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective
Abstract
:1. Introduction
2. The Epidemic of FHB and Its Impact on Wheat Characteristics
2.1. Infection and Broadcasting of Wheat FHB
2.2. Changes in Wheat Characteristics Caused by FHB
3. Detection of Wheat FHB at Microscopic Scale
3.1. Overview of Previous Research
3.2. Challenge
4. Detection of Wheat FHB at Medium Scale
4.1. Overview of Previous Research on Kernels
4.2. Challenges with Kernel Detection
4.3. Overview of Previous Research on Ears
4.3.1. Detection Based on RGB Imaging
4.3.2. Detection Based on Spectral Imaging
Nomination | Scene | Index Formulation | Reference |
---|---|---|---|
FCI | Lab | FCI = 0.25*2(R668 − R417) − R539 | [102] |
FDI | Lab | FDI = (Rλ1 − Rλ2)/(Rλ1 + Rλ2) | [60] |
WFSI and WFTI | Lab | WFSI = (W1 − W2)/(W1 + W2) WFTI = (T1 − T2)/(T1 + T2) | [100] |
WSI | Field (black background) | WSI = (SD450–488 − SD500–540)/ (SD450–488 + SD500–540) | [103] |
WFCI1 and WFCI2 | Field (in situ) | WFSI = (R401 − R840)/(R401+ R840) WFTI = (R460 − R786)/(R460 + R786) | [104] |
WFItwo and WFIthree | Field (in situ) | WFItwo = (R687 − R760)/(R687 + R760) WFIthree = (R760 − R687)/(R687 + R659) | [105] |
4.4. Challenge on Ears Detection
4.4.1. Part of RGB Imaging
4.4.2. Part of Spectral Imaging
5. Detection of Wheat FHB at Submacroscopic Scale
5.1. Overview of Previous Research
5.2. Challenge
6. Detection of Wheat FHB at Macroscopic Scale
6.1. Overview of Previous Research
6.2. Challenge
7. Future Perspectives
8. Final Considerations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Li, B.; Liang, J.; Zhu, Y.; Wang, Y.; Jiao, Z. Epidemiological Analysis and Management Strategies of Fusarium Head Blight of Wheat. Curr. Biotechnol. 2021, 11, 647. [Google Scholar]
- Shewry, P.R. Wheat. J. Exp. Bot. 2009, 60, 1537–1553. [Google Scholar] [CrossRef] [PubMed]
- Senapati, N.; Semenov, M.A.; Halford, N.G.; Hawkesford, M.J.; Asseng, S.; Cooper, M.; Ewert, F.; van Ittersum, M.K.; Martre, P.; Olesen, J.E.; et al. Global wheat production could benefit from closing the genetic yield gap. Nat. Food 2022, 3, 532–541. [Google Scholar] [CrossRef] [PubMed]
- Tian, Z.; Wang, J.W.; Li, J.; Han, B. Designing future crops: Challenges and strategies for sustainable agriculture. Plant J. 2021, 105, 1165–1178. [Google Scholar] [CrossRef] [PubMed]
- Börner, A. Fusarium Head Blight and Rust Diseases in Soft Red Winter Wheat in the Southeast United States: State of the Art, Challenges and Future Perspective for Breeding. Fungal Wheat Dis. Etiol. Breed. Integr. Manag. 2021, 11, 541209. [Google Scholar] [CrossRef] [PubMed]
- Savary, S.; Willocquet, L.; Pethybridge, S.J.; Esker, P.; McRoberts, N.; Nelson, A. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 2019, 3, 430–439. [Google Scholar] [CrossRef]
- Wilson, W.; Dahl, B.; Nganje, W. Economic costs of Fusarium Head Blight, scab and deoxynivalenol. World Mycotoxin J. 2018, 11, 291–302. [Google Scholar] [CrossRef]
- Nganje, W.E.; Bangsund, D.A.; Leistritz, F.L.; Wilson, W.W.; Tiapo, N.M. Regional Economic Impacts of Fusarium Head Blight in Wheat and Barley. Rev. Agric. Econ. 2004, 26, 332–347. [Google Scholar] [CrossRef]
- Robinson, C.V.; Bishop, A.H. A disclosure gel for visual detection of live Bacillus anthracis spores. J. Appl. Microbiol. 2019, 126, 1700–1707. [Google Scholar] [CrossRef]
- Liang, K.; Song, J.; Yuan, R.; Ren, Z. Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat. Sensors 2023, 23, 6600. [Google Scholar] [CrossRef]
- Alisaac, E.; Mahlein, A.-K. Fusarium head blight on wheat: Biology, modern detection and diagnosis and integrated disease management. Toxins 2023, 15, 192. [Google Scholar] [CrossRef] [PubMed]
- Singh, L.; Schulden, T.; Wight, J.P.; Crank, J.; Thorne, L.; Erwin, J.E.; Dong, Y.; Rawat, N. Evaluation of application timing of Miravis Ace for control of Fusarium head blight in wheat. Plant Health Prog. 2021, 22, 94–100. [Google Scholar] [CrossRef]
- Zhang, N.; Yang, G.; Pan, Y.; Yang, X.; Chen, L.; Zhao, C. A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sens. 2020, 12, 3188. [Google Scholar] [CrossRef]
- Dweba, C.C.; Figlan, S.; Shimelis, H.A.; Motaung, T.E.; Sydenham, S.; Mwadzingeni, L.; Tsilo, T.J. Fusarium head blight of wheat: Pathogenesis and control strategies. Crop Prot. 2017, 91, 114–122. [Google Scholar] [CrossRef]
- Zhou, C.; Liang, D.; Yang, X.; Yang, H.; Yue, J.; Yang, G. Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM. Front. Plant Sci. 2018, 9, 1024. [Google Scholar] [CrossRef] [PubMed]
- Goyal, L.; Sharma, C.M.; Singh, A.; Singh, P.K. Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture. Inform. Med. Unlocked 2021, 25, 100642. [Google Scholar] [CrossRef]
- Zhang, D.-Y.; Luo, H.-S.; Cheng, T.; Li, W.-F.; Zhou, X.-G.; Wei, G.; Gu, C.-Y.; Diao, Z. Enhancing wheat Fusarium head blight detection using rotation Yolo wheat detection network and simple spatial attention network. Comput. Electron. Agric. 2023, 211, 107968. [Google Scholar] [CrossRef]
- Maloney, P.V.; Petersen, S.; Navarro, R.A.; Marshall, D.; McKendry, A.L.; Costa, J.M.; Murphy, J.P. Digital Image Analysis Method for Estimation of Fusarium-Damaged Kernels in Wheat. Crop Sci. 2014, 54, 2077–2083. [Google Scholar] [CrossRef]
- Azimi, N.; Sofalian, O.; Davari, M.; Asghari, A.; Zare, N. Statistical and machine learning-based FHB detection in durum wheat. Plant Breed. Biotechnol. 2020, 8, 265–280. [Google Scholar] [CrossRef]
- Aravind, K.R.; Rebecca, L.W.; Abdul, M.M. Detection of Fusarium head blight in wheat using hyperspectral data and deep learning. Expert Syst. Appl. 2022, 208, 118240. [Google Scholar] [CrossRef]
- Li, L.; Chen, S.; Deng, M.; Gao, Z. Optical techniques in non-destructive detection of wheat quality: A review. Grain Oil Sci. Technol. 2022, 5, 44–57. [Google Scholar] [CrossRef]
- Barbedo, J.G.A. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 2016, 144, 52–60. [Google Scholar] [CrossRef]
- Shafi, U.; Mumtaz, R.; Shafaq, Z.; Zaidi, S.M.H.; Kaifi, M.O.; Mahmood, Z.; Zaidi, S.A.R. Wheat rust disease detection techniques: A technical perspective. J. Plant Dis. Prot. 2022, 129, 489–504. [Google Scholar] [CrossRef]
- Zhang, D.-Y.; Zhang, W.; Cheng, T.; Zhou, X.-G.; Yan, Z.; Wu, Y.; Zhang, G.; Yang, X. Detection of wheat scab fungus spores utilizing the Yolov5-ECA-ASFF network structure. Comput. Electron. Agric. 2023, 210, 107953. [Google Scholar] [CrossRef]
- Zhang, H.; Huang, L.; Huang, W.; Dong, Y.; Weng, S.; Zhao, J.; Ma, H.; Liu, L. Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion. Front. Plant Sci. 2022, 13, 1004427. [Google Scholar] [CrossRef] [PubMed]
- Li, L.; Dong, Y.; Xiao, Y.; Liu, L.; Zhao, X.; Huang, W. Combining Disease Mechanism and Machine Learning to Predict Wheat Fusarium Head Blight. Remote Sens. 2022, 14, 2732. [Google Scholar] [CrossRef]
- Hussain, S.; Mustafa, G.; Haider Khan, I.; Liu, J.; Chen, C.; Hu, B.; Chen, M.; Ali, I.; Liu, Y. Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis. Remote Sens. 2023, 15, 3431. [Google Scholar] [CrossRef]
- Zhang, D.; Zhang, W.; Cheng, T.; Lei, Y.; Qiao, H.; Guo, W.; Yang, X.; Gu, C. Segmentation of wheat scab fungus spores based on CRF_ResUNet++. Comput. Electron. Agric. 2024, 216, 108547. [Google Scholar] [CrossRef]
- Ba, W.; Jin, X.; Lu, J.; Rao, Y.; Zhang, T.; Zhang, X.; Zhou, J.; Li, S. Research on predicting early Fusarium head blight with asymptomatic wheat grains by micro-near infrared spectrometer. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2023, 287, 122047. [Google Scholar] [CrossRef] [PubMed]
- Moghimi, A.; Yang, C.; Anderson, J.A.; Reynolds, S.K. Selecting informative spectral bands using machine learning techniques to detect Fusarium head blight in wheat. In Proceedings of the 2019 ASABE Annual International Meeting, Boston, MA, USA, 7–10 July 2019. [Google Scholar]
- Mao, R.; Wang, Z.; Li, F.; Zhou, J.; Chen, Y.; Hu, X. GSEYOLOX-s: An Improved Lightweight Network for Identifying the Severity of Wheat Fusarium Head Blight. Agronomy 2023, 13, 242. [Google Scholar] [CrossRef]
- Huang, L.; Li, T.; Ding, C.; Zhao, J.; Zhang, D.; Yang, G. Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion. Sensors 2020, 20, 2887. [Google Scholar] [CrossRef]
- Vincke, D.; Eylenbosch, D.; Jacquemin, G.; Chandelier, A.; Fernández Pierna, J.A.; Stevens, F.; Baeten, V.; Mercatoris, B.; Vermeulen, P. Near infrared hyperspectral imaging method to assess Fusarium Head Blight infection on winter wheat ears. Microchem. J. 2023, 191, 108812. [Google Scholar] [CrossRef]
- Huang, L.; Zhang, H.; Huang, W.; Dong, Y.; Ye, H.; Ma, H.; Zhao, J. Identification of Fusarium head blight in wheat ears using vertical angle-based reflectance spectroscopy. Arab. J. Geosci. 2021, 14, 423. [Google Scholar] [CrossRef]
- Zhang, D.; Wang, D.; Gu, C.; Jin, N.; Zhao, H.; Chen, G.; Liang, H.; Liang, D. Using Neural Network to Identify the Severity of Wheat Fusarium Head Blight in the Field Environment. Remote Sens. 2019, 11, 2375. [Google Scholar] [CrossRef]
- Dhakal, K.; Sivaramakrishnan, U.; Zhang, X.; Belay, K.; Oakes, J.; Wei, X.; Li, S. Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels. Sensors 2023, 23, 3523. [Google Scholar] [CrossRef]
- Bao, W.; Liu, W.; Yang, X.; Hu, G.; Zhang, D.; Zhou, X. Adaptively spatial feature fusion network: An improved UAV detection method for wheat scab. Precis. Agric. 2023, 24, 1154–1180. [Google Scholar] [CrossRef]
- Bao, W.; Huang, C.; Hu, G.; Su, B.; Yang, X. Detection of Fusarium head blight in wheat using UAV remote sensing based on parallel channel space attention. Comput. Electron. Agric. 2024, 217, 108630. [Google Scholar] [CrossRef]
- Liu, L.; Dong, Y.; Huang, W.; Du, X.; Ma, H. Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery. Remote Sens. 2020, 12, 3811. [Google Scholar] [CrossRef]
- Zhu, W.; Feng, Z.; Dai, S.; Zhang, P.; Wei, X. Using UAV Multispectral Remote Sensing with Appropriate Spatial Resolution and Machine Learning to Monitor Wheat Scab. Agriculture 2022, 12, 1785. [Google Scholar] [CrossRef]
- Xiao, Y.; Dong, Y.; Huang, W.; Liu, L. Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model. Int. J. Appl. Earth Obs. Geoinf. 2022, 114, 103043. [Google Scholar] [CrossRef]
- Xiao, Y.; Dong, Y.; Huang, W.; Liu, L.; Ma, H.; Ye, H.; Wang, K. Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions. Remote Sens. 2020, 12, 3046. [Google Scholar] [CrossRef]
- Parry, D.; Jenkinson, P.; McLeod, L. Fusarium ear blight (scab) in small grain cereals—A review. Plant Pathol. 1995, 44, 207–238. [Google Scholar] [CrossRef]
- Osborne, L.E.; Stein, J.M. Epidemiology of Fusarium head blight on small-grain cereals. Int. J. Food Microbiol. 2007, 119, 103–108. [Google Scholar] [CrossRef] [PubMed]
- Shah, D.A.; De Wolf, E.D.; Paul, P.A.; Madden, L.V. Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States. Phytopathology 2019, 109, 96–110. [Google Scholar] [CrossRef] [PubMed]
- Anderson, J.A. Marker-assisted selection for Fusarium head blight resistance in wheat. Int. J. Food Microbiol. 2007, 119, 51–53. [Google Scholar] [CrossRef] [PubMed]
- Wegulo, S.N. Factors influencing deoxynivalenol accumulation in small grain cereals. Toxins 2012, 4, 1157–1180. [Google Scholar] [CrossRef] [PubMed]
- Gorczyca, A.; Oleksy, A.; Gala-Czekaj, D.; Urbaniak, M.; Laskowska, M.; Waskiewicz, A.; Stepien, L. Fusarium head blight incidence and mycotoxin accumulation in three durum wheat cultivars in relation to sowing date and density. Naturwissenschaften 2017, 105, 2. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Lin, F.; Zhao, Y.; Huang, T.; Ding, L.; Mei, A.; Cheng, X. The population reasons and control measures of wheat scab in the east of Jiangsu. J. Agric. 2015, 5, 33–38. [Google Scholar]
- Leplat, J.; Friberg, H.; Abid, M.; Steinberg, C. Survival of Fusarium graminearum, the causal agent of Fusarium head blight. A review. Agron. Sustain. Dev. 2013, 33, 97–111. [Google Scholar] [CrossRef]
- Tini, F.; Beccari, G.; Onofri, A.; Ciavatta, E.; Gardiner, D.M.; Covarelli, L. Fungicides may have differential efficacies towards the main causal agents of Fusarium head blight of wheat. Pest Manag. Sci. 2020, 76, 3738–3748. [Google Scholar] [CrossRef]
- Berman, M.; Connor, P.; Whitbourn, L.; Coward, D.; Osborne, B.; Southan, M. Classification of sound and stained wheat grains using visible and near infrared hyperspectral image analysis. J. Near Infrared Spectrosc. 2007, 15, 351–358. [Google Scholar] [CrossRef]
- Levasseur-Garcia, C. Updated overview of infrared spectroscopy methods for detecting mycotoxins on cereals (corn, wheat, and barley). Toxins 2018, 10, 38. [Google Scholar] [CrossRef] [PubMed]
- Liu, L.; Dong, Y.; Huang, W.; Du, X.; Ren, B.; Huang, L.; Zheng, Q.; Ma, H. A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery. IEEE Access 2020, 8, 52181–52191. [Google Scholar] [CrossRef]
- Hamila, O.; Henry, C.; Molina, O.I.; Bidinosti, C.P.; Henriquez, M.A. Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3d convolutional neural networks. arXiv 2023, arXiv:2303.05634. [Google Scholar] [CrossRef]
- Ivanova, A.; Shutova, A.; Gannesen, A.; Lebedin, Y.; Eremin, S. Determination of the mycelium and antigens of a number of micromycetes in soil extracts via enzyme-linked immunosorbent assay. Appl. Biochem. Microbiol. 2020, 56, 72–77. [Google Scholar] [CrossRef]
- Quesada, T.; Hughes, J.; Smith, K.; Shin, K.; James, P.; Smith, J. A low-cost spore trap allows collection and real-time PCR quantification of airborne Fusarium circinatum spores. Forests 2018, 9, 586. [Google Scholar] [CrossRef]
- Qiu, R.; Yang, C.; Moghimi, A.; Zhang, M.; Steffenson, B.J.; Hirsch, C.D. Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sens. 2019, 11, 2658. [Google Scholar] [CrossRef]
- Rieker, M.E.G.; Lutz, M.A.; El-Hasan, A.; Thomas, S.; Voegele, R.T. Hyperspectral Imaging and Selected Biological Control Agents for the Management of Fusarium Head Blight in Spring Wheat. Plants 2023, 12, 3534. [Google Scholar] [CrossRef] [PubMed]
- Zhang, D.; Wang, Q.; Lin, F.; Yin, X.; Gu, C.; Qiao, H. Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging. Sensors 2020, 20, 2260. [Google Scholar] [CrossRef]
- Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Rakitin, V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 1999, 106, 135–141. [Google Scholar] [CrossRef]
- Jiang, H.; Zhang, Y.; Wang, W.; Cao, X.; Xu, H.; Liu, H.; Qi, J.; Jiang, C.; Wang, C. FgCsn12 Is Involved in the Regulation of Ascosporogenesis in the Wheat Scab Fungus Fusarium graminearum. Int. J. Mol. Sci. 2022, 23, 10445. [Google Scholar] [CrossRef] [PubMed]
- Korsnes, R.; Westrum, K.; Fløistad, E.; Klingen, I. Computer-assisted image processing to detect spores from the fungus Pandora neoaphidis. MethodsX 2016, 3, 231–241. [Google Scholar] [CrossRef] [PubMed]
- Sujatha, R.; Chatterjee, J.M.; Jhanjhi, N.; Brohi, S.N. Performance of deep learning vs. machine learning in plant leaf disease detection. Microprocess. Microsyst. 2021, 80, 103615. [Google Scholar] [CrossRef]
- Yuan, J.; Huang, Z.; Zhang, D.; Yang, X.; Gu, C. SporeDet: A Real-Time Detection of Wheat Scab Spores. In Proceedings of the International Conference on Intelligent Computing, Zhengzhou, China, 10–13 August 2023; pp. 531–543. [Google Scholar]
- Forrer, H.-R.; Pflugfelder, A.; Musa, T.; Vogelgsang, S. Low-cost spore traps: An efficient tool to manage fusarium head blight through improved cropping systems. Agronomy 2021, 11, 987. [Google Scholar] [CrossRef]
- Cao, X.; Zhou, Y.; Duan, X. The application of volumetric spore trap in plant disease epidemiology. In Proceedings of the 2008 Academic Conference of the Chinese Society of Plant Pathology, Guangzhou, China, 21–27 July 2008. [Google Scholar]
- Nadimi, M.; Brown, J.M.; Morrison, J.; Paliwal, J. Examination of wheat kernels for the presence of Fusarium damage and mycotoxins using near-infrared hyperspectral imaging. Meas. Food 2021, 4, 100011. [Google Scholar] [CrossRef]
- van Bruggen, A.H.; Gamliel, A.; Finckh, M.R. Plant disease management in organic farming systems. Pest Manag. Sci. 2016, 72, 30–44. [Google Scholar] [CrossRef] [PubMed]
- Bernardes, R.C.; De Medeiros, A.; da Silva, L.; Cantoni, L.; Martins, G.F.; Mastrangelo, T.; Novikov, A.; Mastrangelo, C.B. Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology. Agriculture 2022, 12, 1801. [Google Scholar] [CrossRef]
- Wang, D.; Dowell, F.; Chung, D. Assessment of heat-damaged wheat kernels using near-infrared spectroscopy. In Proceedings of the 2001 ASAE Annual Meeting, Philadelphia, PA, USA, 4–7 August 2001; p. 1. [Google Scholar]
- Christensen, C.M.; Kaufmann, H. Deterioration of stored grains by fungi. Annu. Rev. Phytopathol. 1965, 3, 69–84. [Google Scholar] [CrossRef]
- Kalsa, K.K.; Subramanyam, B.; Demissie, G.; Worku, A.F.; Habtu, N.G. Major insect pests and their associated losses in quantity and quality of farm-stored wheat seed. Ethiop. J. Agric. Sci. 2019, 29, 71–82. [Google Scholar]
- Najafian, K.; Jin, L.; Kutcher, H.R.; Hladun, M.; Horovatin, S.; Oviedo-Ludena, M.A.; De Andrade, S.M.P.; Wang, L.; Stavness, I. Detection of Fusarium Damaged Kernels in Wheat Using Deep Semi-Supervised Learning on a Novel WheatSeedBelt Dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 2–6 October 2023; pp. 660–669. [Google Scholar]
- Peiris, K.H.; Pumphrey, M.O.; Dowell, F.E. NIR absorbance characteristics of deoxynivalenol and of sound and Fusarium-damaged wheat kernels. J. Near Infrared Spectrosc. 2009, 17, 213–221. [Google Scholar] [CrossRef]
- Delwiche, S.R.; Rodriguez, I.T.; Rausch, S.R.; Graybosch, R.A. Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging. J. Cereal Sci. 2019, 87, 18–24. [Google Scholar] [CrossRef]
- Liang, K.; Huang, J.; He, R.; Wang, Q.; Chai, Y.; Shen, M. Comparison of Vis-NIR and SWIR hyperspectral imaging for the non-destructive detection of DON levels in Fusarium head blight wheat kernels and wheat flour. Infrared Phys. Technol. 2020, 106, 103281. [Google Scholar] [CrossRef]
- Almoujahed, M.B.; Rangarajan, A.K.; Whetton, R.L.; Vincke, D.; Eylenbosch, D.; Vermeulen, P.; Mouazen, A.M. Non-destructive detection of fusarium head blight in wheat kernels and flour using visible near-infrared and mid-infrared spectroscopy. Chemom. Intell. Lab. Syst. 2024, 245, 105050. [Google Scholar] [CrossRef]
- Craig, A.P.; Franca, A.S.; Irudayaraj, J. Surface-enhanced Raman spectroscopy applied to food safety. Annu. Rev. Food Sci. Technol. 2013, 4, 369–380. [Google Scholar] [CrossRef] [PubMed]
- Qiu, M.; Zheng, S.; Tang, L.; Hu, X.; Xu, Q.; Zheng, L.; Weng, S. Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels. Foods 2022, 11, 578. [Google Scholar] [CrossRef] [PubMed]
- Barbedo, J.G.A.; Tibola, C.S.; Fernandes, J.M.C. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosyst. Eng. 2015, 131, 65–76. [Google Scholar] [CrossRef]
- Hughes, D.; Salathé, M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv 2015, arXiv:1511.08060. [Google Scholar]
- Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using deep learning for image-based plant disease detection. Front. Plant Sci. 2016, 7, 215–232. [Google Scholar] [CrossRef] [PubMed]
- Ferentinos, K.P. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 2018, 145, 311–318. [Google Scholar] [CrossRef]
- Su, W.-H.; Zhang, J.; Yang, C.; Page, R.; Szinyei, T.; Hirsch, C.D.; Steffenson, B.J. Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sens. 2020, 13, 26. [Google Scholar] [CrossRef]
- Abdalla, A.K.A.; Azad, B.; Won, K.; Nafchi, A. Maintaining Optimum Closeup in Wheat FHB Detection Using 360-Degree Deep Scanning Method. In Proceedings of the 2023 ASABE Annual International Meeting, Omaha, NE, USA, 9–12 July 2023. [Google Scholar]
- Gao, Y.; Wang, H.; Li, M.; Su, W.-H. Automatic Tandem Dual BlendMask Networks for Severity Assessment of Wheat Fusarium Head Blight. Agriculture 2022, 12, 1493. [Google Scholar] [CrossRef]
- Rößle, D.; Prey, L.; Ramgraber, L.; Hanemann, A.; Cremers, D.; Noack, P.O.; Schön, T. Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset. Plant Phenom. 2023, 5, 68. [Google Scholar] [CrossRef] [PubMed]
- Gu, C.; Wang, D.; Zhang, H.; Zhang, J.; Zhang, D.; Liang, D. Fusion of Deep Convolution and Shallow Features to Recognize the Severity of Wheat Fusarium Head Blight. Front. Plant Sci. 2020, 11, 599886. [Google Scholar] [CrossRef] [PubMed]
- Zhang, D.; Luo, H.; Wang, D.; Zhou, X.; Li, W.; Gu, C.; Zhang, G.; He, F. Assessment of the levels of damage caused by Fusarium head blight in wheat using an improved YoloV5 method. Comput. Electron. Agric. 2022, 198, 107086. [Google Scholar] [CrossRef]
- Gao, C.; Guo, W.; Yang, C.; Gong, Z.; Yue, J.; Fu, Y.; Feng, H. A fast and lightweight detection model for wheat fusarium head blight spikes in natural environments. Comput. Electron. Agric. 2024, 216, 108484. [Google Scholar] [CrossRef]
- Chen, Y.; Jiang, H.; Li, C.; Jia, X.; Ghamisi, P. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 2016, 54, 6232–6251. [Google Scholar] [CrossRef]
- Bauriegel, E.; Giebel, A.; Geyer, M.; Schmidt, U.; Herppich, W.B. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 2011, 75, 304–312. [Google Scholar] [CrossRef]
- Huang, L.; Wu, K.; Huang, W.; Dong, Y.; Ma, H.; Liu, Y.; Liu, L. Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM. Agriculture 2021, 11, 998. [Google Scholar] [CrossRef]
- Almoujahed, M.B.; Rangarajan, A.K.; Whetton, R.L.; Vincke, D.; Eylenbosch, D.; Vermeulen, P.; Mouazen, A.M. Detection of fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning. Comput. Electron. Agric. 2022, 203, 107456. [Google Scholar] [CrossRef]
- Ma, H.; Huang, W.; Jing, Y.; Pignatti, S.; Laneve, G.; Dong, Y.; Ye, H.; Liu, L.; Guo, A.; Jiang, J. Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis. Sensors 2019, 20, 20. [Google Scholar] [CrossRef]
- Zhang, D.-Y.; Chen, G.; Yin, X.; Hu, R.-J.; Gu, C.-Y.; Pan, Z.-G.; Zhou, X.-G.; Chen, Y. Integrating spectral and image data to detect Fusarium head blight of wheat. Comput. Electron. Agric. 2020, 175, 105588. [Google Scholar] [CrossRef]
- Mustafa, G.; Zheng, H.; Li, W.; Yin, Y.; Wang, Y.; Zhou, M.; Liu, P.; Bilal, M.; Jia, H.; Li, G. Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods. Front. Plant Sci. 2023, 13, 1102341. [Google Scholar] [CrossRef] [PubMed]
- Alisaac, E.; Behmann, J.; Kuska, M.T.; Dehne, H.-W.; Mahlein, A.-K. Hyperspectral quantification of wheat resistance to Fusarium head blight: Comparison of two Fusarium species. Eur. J. Plant Pathol. 2018, 152, 869–884. [Google Scholar] [CrossRef]
- Mustafa, G.; Zheng, H.; Khan, I.H.; Zhu, J.; Yang, T.; Wang, A.; Xue, B.; He, C.; Jia, H.; Li, G.; et al. Enhancing fusarium head blight detection in wheat crops using hyperspectral indices and machine learning classifiers. Comput. Electron. Agric. 2024, 218, 108663. [Google Scholar] [CrossRef]
- Mahlein, A.K.; Alisaac, E.; Al Masri, A.; Behmann, J.; Dehne, H.W.; Oerke, E.C. Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale. Sensors 2019, 19, 2281. [Google Scholar] [CrossRef] [PubMed]
- Zhang, N.; Pan, Y.; Feng, H.; Zhao, X.; Yang, X.; Ding, C.; Yang, G. Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets. Biosyst. Eng. 2019, 186, 83–99. [Google Scholar] [CrossRef]
- Huang, L.; Zhang, H.; Ding, W.; Huang, W.; Hu, T.; Zhao, J. Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset. J. Spectrosc. 2019, 2019, 9153195. [Google Scholar] [CrossRef]
- Mustafa, G.; Zheng, H.; Khan, I.H.; Tian, L.; Jia, H.; Li, G.; Cheng, T.; Tian, Y.; Cao, W.; Zhu, Y.; et al. Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning. Remote Sens. 2022, 14, 2784. [Google Scholar] [CrossRef]
- Zhang, H.; Zhao, J.; Huang, L.; Huang, W.; Dong, Y.; Ma, H.; Ruan, C. Development of new indices and use of CARS-Ridge algorithm for wheat fusarium head blight detection using in-situ hyperspectral data. Biosyst. Eng. 2024, 237, 13–25. [Google Scholar] [CrossRef]
- Jin, X.; Jie, L.; Wang, S.; Qi, H.; Li, S. Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field. Remote Sens. 2018, 10, 395. [Google Scholar] [CrossRef]
- Huertas-Tato, J.; Martín, A.; Fierrez, J.; Camacho, D. Fusing CNNs and statistical indicators to improve image classification. Inf. Fusion 2022, 79, 174–187. [Google Scholar] [CrossRef]
- Zarco-Tejada, P.J.; Camino, C.; Beck, P.; Calderon, R.; Hornero, A.; Hernández-Clemente, R.; Kattenborn, T.; Montes-Borrego, M.; Susca, L.; Morelli, M. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 2018, 4, 432–439. [Google Scholar] [CrossRef] [PubMed]
- Poblete, T.; Camino, C.; Beck, P.; Hornero, A.; Kattenborn, T.; Saponari, M.; Boscia, D.; Navas-Cortes, J.A.; Zarco-Tejada, P. Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis. ISPRS J. Photogramm. Remote Sens. 2020, 162, 27–40. [Google Scholar] [CrossRef]
- Jin, X.; Xiong, J.; Rao, Y.; Zhang, T.; Ba, W.; Gu, S.; Zhang, X.; Lu, J. TranNas-NirCR: A method for improving the diagnosis of asymptomatic wheat scab with transfer learning and neural architecture search. Comput. Electron. Agric. 2023, 213, 108271. [Google Scholar] [CrossRef]
- Roosjen, P.P.; Brede, B.; Suomalainen, J.M.; Bartholomeus, H.M.; Kooistra, L.; Clevers, J.G. Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data–potential of unmanned aerial vehicle imagery. Int. J. Appl. Earth Obs. Geoinf. 2018, 66, 14–26. [Google Scholar] [CrossRef]
- Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 2014, 55, 89–99. [Google Scholar] [CrossRef]
- O’Grady, M.; Langton, D.; O’Hare, G. Edge computing: A tractable model for smart agriculture? Artif. Intell. Agric. 2019, 3, 42–51. [Google Scholar] [CrossRef]
- Hadidi, R.; Cao, J.; Xie, Y.; Asgari, B.; Krishna, T.; Kim, H. Characterizing the deployment of deep neural networks on commercial edge devices. In Proceedings of the 2019 IEEE International Symposium on Workload Characterization (IISWC), Orlando, FL, USA, 3–5 November 2019; pp. 35–48. [Google Scholar]
- Feng, W.; Shen, W.; He, L.; Duan, J.; Guo, B.; Li, Y.; Wang, C.; Guo, T. Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices. Precis. Agric. 2016, 17, 608–627. [Google Scholar] [CrossRef]
- Bauriegel, E.; Herppich, W. Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat. Agriculture 2014, 4, 32–57. [Google Scholar] [CrossRef]
- Hong, Q.; Jiang, L.; Zhang, Z.; Ji, S.; Gu, C.; Mao, W.; Li, W.; Liu, T.; Li, B.; Tan, C. A Lightweight Model for Wheat Ear Fusarium Head Blight Detection Based on RGB Images. Remote Sens. 2022, 14, 3481. [Google Scholar] [CrossRef]
- Yan, Z.; Zhang, H.; Van Der Lee, T.; Waalwijk, C.; Van Diepeningen, A.; Deng, Y.; Feng, J.; Liu, T.; Chen, W. Resistance to Fusarium head blight and mycotoxin accumulation among 129 wheat cultivars from different ecological regions in China. World Mycotoxin J. 2020, 13, 189–200. [Google Scholar] [CrossRef]
- Gao, C.; Ji, X.; He, Q.; Gong, Z.; Sun, H.; Wen, T.; Guo, W. Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery. Agriculture 2023, 13, 293. [Google Scholar] [CrossRef]
- Xiao, Y.; Dong, Y.; Huang, W.; Liu, L.; Ma, H. Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sens. 2021, 13, 2437. [Google Scholar] [CrossRef]
- Zhang, D.; Zhou, X.; Zhang, J.; Lan, Y.; Xu, C.; Liang, D. Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS ONE 2018, 13, e0187470. [Google Scholar] [CrossRef] [PubMed]
- Liu, L.; Dong, Y.; Huang, W.; Du, X.; Luo, J.; Shi, Y.; Ma, H. Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method. Remote Sens. 2019, 11, 298. [Google Scholar] [CrossRef]
- Griffiths, P.; Nendel, C.; Hostert, P. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sens. Environ. 2019, 220, 135–151. [Google Scholar] [CrossRef]
- Araghi, A.; Maghrebi, M.; Olesen, J.E. Effect of wind speed variation on rainfed wheat production evaluated by the CERES-Wheat model. Int. J. Biometeorol. 2022, 66, 225–233. [Google Scholar] [CrossRef] [PubMed]
- Zhao, F.; Yang, G.; Yang, H.; Long, H.; Xu, W.; Zhu, Y.; Meng, Y.; Han, S.; Liu, M. A Method for Prediction of Winter Wheat Maturity Date Based on MODIS Time Series and Accumulated Temperature. Agriculture 2022, 12, 945. [Google Scholar] [CrossRef]
- Savary, S.; Stetkiewicz, S.; Brun, F.; Willocquet, L. Modelling and mapping potential epidemics of wheat diseases—Examples on leaf rust and Septoria tritici blotch using EPIWHEAT. Eur. J. Plant Pathol. 2015, 142, 771–790. [Google Scholar] [CrossRef]
- Halcro, K.; McNabb, K.; Lockinger, A.; Socquet-Juglard, D.; Bett, K.E.; Noble, S.D. The BELT and phenoSEED platforms: Shape and colour phenotyping of seed samples. Plant Methods 2020, 16, 49. [Google Scholar] [CrossRef]
- Torres, A.M.; Palacios, S.A.; Yerkovich, N.; Palazzini, J.M.; Battilani, P.; Leslie, J.; Logrieco, A.; Chulze, S.N. Fusarium head blight and mycotoxins in wheat: Prevention and control strategies across the food chain. World Mycotoxin J. 2019, 12, 333–355. [Google Scholar] [CrossRef]
- Ming, R.; Jiang, R.; Luo, H.; Lai, T.; Guo, E.; Zhou, Z. Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms. Agronomy 2023, 13, 2499. [Google Scholar] [CrossRef]
- Feng, G.; Wang, C.; Wang, A.; Gao, Y.; Zhou, Y.; Huang, S.; Luo, B. Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network. Agriculture 2024, 14, 244. [Google Scholar] [CrossRef]
- Xu, J.; Gu, B.; Tian, G. Review of agricultural IoT technology. Artif. Intell. Agric. 2022, 6, 10–22. [Google Scholar] [CrossRef]
Imaging Scale | Filming System | Detection Task | Evaluation Metric | Reference |
---|---|---|---|---|
Microscopic | Microscope with digital camera | Wheat FHB fungus detection | Accuracy 0.9857 | [24] |
Electron microscope | Wheat FHB spore segmentation | F1 0.943; mIoU 0.925 | [28] | |
Micro-near-infrared spectrometer | Early FHB with asymptomatic grains prediction | mAP 0.88 | [29] | |
Hyperspectral camera | Healthy and diseased ears discrimination | Accuracy 0.99 | [30] | |
Medium | Digital camera and mobile phone | Severity of wheat ears FHB identification | mAP 0.9923 | [31] |
Spectrometer and digital CCD camera | Severity of wheat ears FHB Diagnosis | Accuracy 0.92 | [32] | |
NIR camera | Three classes of FHB severity discrimination | Sensitivity 0.994; Specificity 0.919 | [33] | |
Portable spectrometer | Wheat ears FHB identification | Accuracy and Kappa: leafy 0.65, 0.27; leafless 0.81, 0.63 | [34] | |
SLR camera | Severity of wheat ears FHB identification | Accuracy 0.925 | [35] | |
Benchtop hyperspectral imaging system | Analysis of damaged wheat kernels | mAP 0.97 | [36] | |
Submacroscopic | UAV with RGB camera at 4 m | Wheat FHB ears detection | AP 0.808; Recall 0.743; Precision 0.779 | [37] |
UAV with RGB sensor at 4 m | Wheat FHB ears detection | mAP 0.832; Recall 0.745; Precision 0.806 | [38] | |
UAV with multispectral camera at 60 m | Wheat FHB monitoring | Overall accuracy 0.98 | [39] | |
UAV with hyperspectral camera at 60 m | Wheat FHB detection | Accuracy 0.83 | [25] | |
UAV with multispectral camera at 20–110 m | Wheat FHB monitoring | R2 0.83; RMSE 3.35; RPD 2.72 | [40] | |
Macroscopic | Satellite MODIS and Sentinel-2 and 3 | Wheat FHB prediction | Overall accuracy 0.88 in April and 0.92 in May | [26] |
Satellite MODIS and Sentinel-2 | Wheat FHB regional prediction | RMSE 0.131; Acc 0.860 | [41] | |
Satellite MODIS and Landsat-8 | Wheat FHB severity prediction | mAP 0.8175 | [42] |
Detection Task | Feature Type | Evaluation Metric | Reference |
---|---|---|---|
Severity monitoring | 5VIs + 1TF + 1SB | AUC 1.0, SD 0.0 and Accuracy 0.98 | [39] |
Disease detection | 10VIs + 3TIs | Accuracy 93.63% and F1-score 92.63% | [119] |
Severity monitoring | 5VIs + 9TFs | R2 0.83, RMSE 3.35 and RPD 2.72 | [40] |
Severity monitoring | 3SFs + 3TFs + 2CFs | Accuracy 85% | [38] |
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Feng, G.; Gu, Y.; Wang, C.; Zhou, Y.; Huang, S.; Luo, B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. Plants 2024, 13, 1722. https://doi.org/10.3390/plants13131722
Feng G, Gu Y, Wang C, Zhou Y, Huang S, Luo B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. Plants. 2024; 13(13):1722. https://doi.org/10.3390/plants13131722
Chicago/Turabian StyleFeng, Guoqing, Ying Gu, Cheng Wang, Yanan Zhou, Shuo Huang, and Bin Luo. 2024. "Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective" Plants 13, no. 13: 1722. https://doi.org/10.3390/plants13131722
APA StyleFeng, G., Gu, Y., Wang, C., Zhou, Y., Huang, S., & Luo, B. (2024). Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. Plants, 13(13), 1722. https://doi.org/10.3390/plants13131722