UAV-based disease detection in strawberry by multispectral image segmentation invoking deep learning

Accurate and timely diagnosis of strawberry diseases is crucial for minimizing crop loss and reducing the cost of plant treatment. The use of unmanned aerial vehicles (UAVs) equipped with multispectral cameras opens up new possibilities for noninvasive plant health monitoring. The objective of this research is to explore the feasibility of detecting diseases in strawberries through multispectral imagery obtained from UAVs combined with deep learning techniques. Leaf spot disease, caused by Ramularia tulasnei Sacc., was chosen as a case study. A DJI Phantom 4 Multispectral quadcopter was used to survey an experimental plot (37 × 22 m) from an altitude of 3 m, achieving a resolution of 2.2 mm per pixel. Preprocessing of the acquired data involved orthomosaic building using Agisoft Metashape Pro and manual region-of-interest (ROI) extraction in ENVI 5.2. A dataset comprising 360 multispectral images, each of size 64 × 64 × 5 (with spectral bands at 450, 560, 650, 730, and 840 nm), was created and separated into three classes: healthy plants, diseased plants, and soil. Using this dataset, a U-Net-based model – supplemented with the attention gate, squeeze-and-excitation, and residual connections modules – was trained. Additionally, to assess the contribution of multispectral channels, another U-Net model was trained on RGB images, as well as a classic Random Forest model based on 40 GLCM texture features. The U-Net model trained on multispectral data achieved an overall classification accuracy of 96.04 %, which is significantly higher than the performance of Random Forest (76.64 %) and the U-Net on RGB images (94.14 %). The resulting disease distribution maps enable site-specific fungicide application. The proposed approach confirmed the effectiveness of deep learning for diagnosing leaf spot disease in strawberries. Future research will focus on expanding the dataset (different cultivars and disease stages), testing more advanced segmentation architectures, and scaling the method to commercial plantations.

1. Abade A., Ferreira P.A., Vidal F.B. Plant diseases recognition on images using convolutional neural networks: a systematic review. Comput Electron Agric. 2021;185:106125. doi 10.1016/j.compag.2021.106125

2. Behmann J., Mahlein A.K., Rumpf T., Römer C., Plümer L. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis Agric. 2015;16:239-260. doi 10.1007/s11119-014-9372-7

3. Benos L., Tagarakis A.C., Dolias G., Berruto R., Kateris D., Bochtis D. Machine learning in agriculture: a comprehensive updated review. Sensors. 2021;21(11):3758. doi 10.3390/s21113758

4. Breiman L. Random forest. Mach Learn. 2001;45:5-32. doi 10.1023/A:1010933404324

5. Candiago S., Remondino F., De Giglio M., Dubbini M., Gattelli M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 2015;7(4): 4026-4047. doi 10.3390/rs70404026

6. Cheshkova A.F. A review of hyperspectral image analysis techniques for plant disease detection and identification. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov J Genet Breed. 2022;26(2): 202-213. doi 10.18699/VJGB-22-25

7. Cheshkova A.F., Riksen V.S. Strawberry disease detection using multispectral UAV imagery. Sel’skokhozyajstvennye Mashiny i Technologii = Agricultural Machinery and Technologies. 2025;19(2):45-52. doi 10.22314/2073-7599-2025-19-2-45-52 (in Russian)

8. Ding W., Abdel-Basset M., Alrashdi I., Hawas H. Next generation of computer vision for plant disease monitoring in precision agriculture: a contemporary survey, taxonomy, experiments, and future direction. Inf Sci. 2024;665:120338. doi 10.1016/j.ins.2024.120338

9. Guo A., Huang W., Dong Y., Ye H., Ma H., Liu B., Wu W., Ren Y., Ruan C., Geng Y. Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sens. 2021;13(1):123. doi 10.3390/rs13010123

10. Hou F.J., Zhang Y., Zhou Y., Zhang M., Lv B., Wu J.Q. Review on infrared imaging technology. Sustainability. 2022;14(18):26. doi 10.3390/su141811161

11. Jin S.C., Sun X.L., Wu F.F., Su Y.J., Li Y.M., Song S.L., Xu K.X., Ma Q., Baret F., Jiang D. Lidar sheds new light on plant phenomics for plant breeding and management: recent advances and future prospects. ISPRS J Photogramm Remote Sens. 2021;171:202-223. doi 10.1016/j.isprsjprs.2020.11.006

12. Kerkech M., Hafiane A., Canals R. VddNet: Vine Disease Detection Network based on multispectral images and depth map. Remote Sens. 2020;12(20):3305. doi 10.3390/rs12203305

13. Lan Y., Huang Z., Deng X., Zhu Z., Huang H., Zheng Z., Lian B., Zeng G., Tong Z. Comparison of machine learning methods for citrus greening detection on UAV multispectral images. Comput Electron Agric. 2020;171:105234. doi 10.1016/j.compag.2020.105234

14. Li S., Yuan F., Ata-UI-Karim S.T., Zheng H., Cheng T., Liu X., Tian Y., Zhu Y., Cao W., Cao Q. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens. 2019;11(15):1763. doi 10.3390/rs11151763

15. Liao K., Yang F., Dang H., Wu Y., Luo K., Li G. Detection of eucalyptus leaf disease with UAV multispectral imagery. Forests. 2022; 13(8):1322. doi 10.3390/f13081322

16. Loey M., Elsawy A., Afify M. Deep learning in plant diseases detection for agricultural crops: a survey. Int J Serv Sci Manage Eng Technol. 2020;11(2):18. doi 10.4018/IJSSMET.2020040103

17. Mahlein A.-K., Rumpf T., Welke P., Dehne H.-W., Plümer L., Steiner U., Oerke E.-C. Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ. 2013;128: 21-30. doi 10.1016/j.rse.2012.09.019

18. Maxwell A.E., Farhadpour S., Das S., Yang Y. geodl: an R package for geospatial deep learning semantic segmentation using torch and terra. PLoS One. 2024;19(12):e0315127. doi 10.1371/journal.pone.0315127

19. Moustakas M., Calatayud Á., Guidi L. Chlorophyll fluorescence imaging analysis in biotic and abiotic stress. Front Plant Sci. 2021;12: 658500. doi 10.3389/fpls.2021.658500

20. Mudarisov S.G., Miftakhov I.R. Deep learning methods and UAV technologies for crop disease detection. Sel’skokhozyajstvennye Mashiny i Technologii = Agricultural Machinery and Technologies. 2024;18(4):24-33. doi 10.22314/2073-7599-2024-18-4-24-33 (in Russian)

21. Nagaraju M., Chawla P. Systematic review of deep learning techniques in plant disease detection. Int J Syst Assur Eng Manage. 2020; 11(3):547-560. doi 10.1007/s13198-020-00972-1

22. Neupane K., Baysal-Gurel F. Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: a review. Remote Sens. 2021;13(19):3841. doi 10.3390/rs13193841

23. Ozherelev V.N., Ozhereleva M.V., Grin A.M., Somin V.V. Dynamics of garden strawberries production all over the world. Vestnik of the Bryansk State Agricultural Academy. 2019;4(74):60-66 (in Russian)

24. Phang S.K., Chiang T.H.A., Happonen A., Chang M.M.L. From satellite to UAV-based remote sensing: a review on precision agriculture. IEEE Access. 2023;11:127057-127076. doi 10.1109/ACCESS.2023.3330886

25. Radoglou-Grammatikis P., Sarigiannidis P., Lagkas T., Moscholios I. A compilation of UAV applications for precision agriculture. Comput Networks. 2020;172:107148. doi 10.1016/j.comnet.2020.107148

26. Ronneberger O., Fischer P., Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science. Vol. 9351. Springer, 2015;234-241. doi 10.1007/978-3-319-24574-4_28

27. Sajitha P., Andrushia A.D., Anand N., Naser M.Z. A review on machine learning and deep learning image-based plant disease classification for industrial farming systems. J Ind Inf Integr. 2024;38:100572. doi 10.1016/j.jii.2024.100572

28. Saleem M.H., Potgieter J., Arif K.M. plant disease detection and classification by deep learning. Plants. 2019;8(11):468. doi 10.3390/plants8110468

29. Singh A., Ganapathysubramanian B., Singh A.K., Sarkar S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 2016;21(2):110-124. doi 10.1016/j.tplants.2015.10.015

30. Su J., Yi D., Su B., Mi Z., Liu C., Hu X., Xu X., Guo L., Chen W.H. Aerial visual perception in smart farming: field study of wheat yellow rust monitoring. IEEE Trans Ind Inf. 2020;17(3):2242-2249. doi 10.1109/TII.20

31. Toscano F., Fiorentino C., Capece N., Erra U., Travascia D., Scopa A., Drosos M., D’Antonio P. Unmanned aerial vehicle for precision agriculture: a review. IEEE Access. 2024;12:69188-69205. doi 10.1109/ACCESS.2024.3401018

32. Zahir S.A.D.M., Omar A.F., Jamlos M.F., Azmi M.A.M., Muncan J. A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection. Sens Actuators A. 2022;338:113468. doi 10.1016/j.sna.2022.113468

33. Zhang S., Li X., Ba Y., Lyu X., Zhang M., Li M. Banana fusarium wilt disease detection by supervised and unsupervised methods from UAV-based multispectral imagery. Remote Sens. 2022;14(5):1231. doi 10.3390/rs14051231

34. Zhang T., Xu Z., Su J., Yang Z., Liu C., Chen W.-H., Li J. Ir-UNet: irregular segmentation U-shape network for wheat yellow rust detection by UAV multispectral imagery. Remote Sens. 2021;13(19): 3892. doi 10.3390/rs13193892

35. Zheng C., Abd-Elrahman A., Whitaker V. Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming. Remote Sens. 2021;13(3):531. doi 10.3390/rs13030531

36. Zhou C., Gong Y., Fang S., Yang K., Peng Y., Wu X., Zhu R. Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index. Front Plant Sci. 2022;13: 957870. doi 10.3389/fpls.2022.957870