REVIEW
Selective protection of cereals using artificial neural networks
More details
Hide details
1
Actuaro Ltd, Warsaw, Poland
2
Plant Biology, W. Szafer Institute of Botany Polish Academy of Sciences, Krakow, Poland
3
Department of Organic Agriculture and Environmental Protection, Institute of Plant Protection – NIR, Poznań, Poland
4
4Robot Ltd., Kęty, Poland
5
Method of Pests Forecasting, Institute of Plant Protection – NIR, Poznań, Poland
6
Agricultural Advisory Centre in Brwinów, WSG University Bydgoszcz, Warsaw, Poland
These authors had equal contribution to this work
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: 2024-09-17
Acceptance date: 2024-11-14
Online publication date: 2025-06-24
HIGHLIGHTS
- Spectral imaging technologies are crucial for early detection of plant diseases
- Hyperspectral imaging reveals changes in plants before visible symptoms appear
- Integration of hyperspectral imaging with AI enhances precision in plant protection
- UAVs offer complementary advantages for field-based selective plant protection
- Future research should address UAV-based precision spraying and monitoring
KEYWORDS
TOPICS
ABSTRACT
Global agricultural losses due to pests and pathogens are substantial, particularly for wheat,
maize, and potatoes. Addressing these challenges necessitates innovative approaches in
plant protection, particularly through early detection methods. This article outlines research
areas concerning the application of spectral imaging technologies in selective crop
protection processes. Recent technological advancements, driven by the development of
high-resolution optical sensors and data analysis methods (Pena et al. 2013), have enabled
early detection of weeds, plant diseases, and pests in the field. Spectral imaging technologies,
particularly hyperspectral imaging, play a pivotal role in early disease detection by capturing
detailed spectral data across a wide range of wavelengths. This technology enables the detection
of subtle physiological changes in plants long before visible symptoms occur. Hyperspectral
imaging has proven effective in identifying diseases such as Fusarium head blight
in wheat, allowing for timely interventions and potentially reducing yield losses. The integration
of hyperspectral imaging with remote sensing technologies, including unmanned
aerial vehicles and ground-based sensors, as well as artificial intelligence represents a significant
advancement in precision agriculture. This multidisciplinary approach aims to enhance
crop protection while minimizing environmental impacts. The article also explores
the advantages and limitations of these technologies and strategies for reducing the reliance
on chemical plant protection methods in agricultural production. It is underlined, that future
research should focus on optimizing these technologies, addressing cost barriers, and
exploring UAV-based applications for precision spraying and monitoring.
FUNDING
This work was supported by the project “Artificial
Intelligence for the identification of undesirable phenomena
and selective crop protection” (Akronim: AI-
4Crop) funded under the “Infostrateg VI”, agreement
no. INFOSTRTEG 6/0014/2023/A, financed by the National
Centre for Research and Development (NCBR).
RESPONSIBLE EDITOR
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (65)
1.
Baratov R., Valixanova H. 2023. Smart system for early detection of agricultural plant diseases in the vegetation period. E3S Web of Conferences. DOI:
https://doi.org/10.1051/e3scon....
2.
Barbedo J.G.A. 2016. A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering 144: 52–60. DOI:
https://doi.org/10.1016/j.bios....
3.
Bartosiak S.F., Arseniuk E., Szechyńska-Hebda M., Bartosiak E. 2021. Monitoring of natural occurrence and severity of leaf and glume blotch diseases of winter wheat and winter triticale incited by necrotrophic fungi Parastagonospora spp. and Zymoseptoria tritici. Agronomy 11 (5): 967. DOI:
https://doi.org/10.3390/agrono....
4.
Battleday R., Peterson J., Griffiths T. 2021. From convolutional neural networks to models of higher‐level cognition (and back again). Annals of the New York Academy of Sciences 1505: 55–78. DOI:
https://doi.org/10.1111/nyas.1....
5.
Bauriegel E., Giebel A., Herppich W.B. 2011. Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears. Sensors 11 (4): 3765–3779. DOI:
https://doi.org/10.3390/s11040....
6.
Behmann J., Mahlein A.K., Rumpf T., Römer C., Plümer L. 2015a. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture 16: 239–260. DOI:
https://doi.org/10.1007/s11119....
7.
Behmann J., Steinrücken J., Plümer L. 2015b. Detection of early plant stress responses in hyperspectral images. Remote Sensing 7 (4): 3872–3896. DOI:
https://doi.org/10.1016/j.ispr....
8.
Bohnenkamp D., Behmann J., Mahlein A. 2019. In-field detection of yellow rust in wheat on the ground canopy and UAV scale. Remote Sensing 11: 2495. DOI:
https://doi.org/10.3390/rs1121....
9.
Bomberski M. 2020. The role of agricultural advisory services in implementing the objectives of the network for innovation in agriculture: a case study of the kuyavian-pomeranian voivodeship. PhD Thesis. Publishing House of the University of Technology and Life Sciences in Bydgoszcz.
10.
Bomberski M. 2023. Implementing innovations and managing operational groups by innovation brokers within the Rural Development Program for 2014-2020 and the Common Agricultural Policy for 2023-2027. Publishing Group of the University of Economy in Bydgoszcz.
11.
Camps-Valls G., Campos-Taberner M., Moreno-Martínez Á., Walther S., Duveiller G., Cescatti A., Mahecha M., Muñoz-Marí J., García-Haro F., Guanter L., Jung M., Gamon J., Reichstein M., Running S. 2021. A unified vegetation index for quantifying the terrestrial biosphere. Science Advances 7 (9): eabc7447. DOI:
https://doi.org/10.1126/sciadv....
12.
Chen J., Chen J., Zhang D., Sun Y., Nanehkaran Y. 2020. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture 173: 105393. DOI:
https://doi.org/10.1016/j.comp....
13.
Chen Z., Yang X., Li F., Cheng X., Hu Q., Miao Z., Xie R., Wu X., Wang K., Song Z., Sun H., Zhuang Z., Yang Y., Xu J., Yin L., Zhou W. 2022. CloudJump: Optimizing Cloud Databases for Cloud Storages. Proceedings of the VLDB Endowment 15: 3432–3444. DOI:
https://doi.org/10.14778/35548....
14.
Cieślik R. 2023. Drones over Polish fields. Poznań University of Life Sciences. [Available on:
https://puls.edu.pl/aktualno-c...] [Accession: May 15, 2024] (in Polish).
15.
Cooper J., Du C., Beaver Z., Zheng M., Page R., Wodarek J., Matny O., Szinyei T., Quiñones A., Anderson J., Smith K., Yang C., Steffenson B., Hirsch C. 2023. An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat. bioRxiv. DOI:
https://doi.org/10.1101/2023.0....
16.
Cravero A., Pardo S., Galeas P., López Fenner J., Caniupán M. 2022. Data type and data sources for agricultural big data and machine learning. Sustainability 14 (23): 16131. DOI:
https://doi.org/10.3390/su1423....
17.
Dasgupta I., Saha J., Venkatasubbu P., Ramasubramanian P. 2020. AI crop predictor and weed detector using wireless technologies: a smart application for farmers. Arabian Journal for Science and Engineering 45: 11115–11127. DOI:
https://doi.org/10.1007/s13369....
18.
Dyda M., Wąsek I., Tyrka M., Wędzony M., Szechyńska-Hebda M. 2019. Local and systemic regulation of PSII efficiency in triticale infected by the hemibiotrophic pathogen Microdochium nivale. Physiologia Plantarum 165 (4): 711–727. DOI:
https://doi.org/10.1111/ppl.12....
19.
Galieni A., D’ascenzo N., Stagnari F., Pagnani G., Xie Q., Pisante M. 2021. Past and future of plant stress detection: an overview from remote sensing to positron emission tomography. Frontiers in Plant Science 11: 609155. DOI:
https://doi.org/10.3389/fpls.2....
20.
Gao C.F., Ji X.J., He Q., Gong Z., Sun H., Wen T., Guo W. 2023. Monitoring of wheat Fusarium head blight on spectral and textural analysis of UAV multispectral imagery. Agriculture 13 (2): 293. DOI:
https://doi.org/10.3390/agricu....
21.
Garg G., Gupta S., Mishra P., Vidyarthi A., Singh A., Ali A. 2023. CROPCARE: An intelligent real-time sustainable iot system for crop disease detection using mobile vision. IEEE Internet of Things Journal 10: 2840–2851. DOI:
https://doi.org/10.1109/JIOT.2....
23.
Golka W., Arseniuk E., Golka A., Góral T. 2020. Artificial neural networks and remote sensing in the assessment of spring wheat infection by Fusarium head blight. Biuletyn IHAR 288: 67–75. (in Polish).
24.
Golka W., Szechyńska-Hebda M., Golka A., Kowalska J., Góral T. 2024a. Cereal health assessment system for farm. Topics in Agricultural Advisory Services (Zagadnienia Doradztwa Rolniczego) 1 (115): 98–118. (in Polish).
25.
Golka W., Szechyńska-Hebda M., Golka A., Kowalska J., Góral T., Bomberski A. 2024b. Analysis of the Teledis system in the assessment of cereal health. Topics in Agricultural Advisory Services (Zagadnienia Doradztwa Rolniczego) 2: 123–135. (in Polish).
27.
Huded S., Savadatti E.H.,S., Deb L., Borah A., Singh V., Panigrahi C. 2023. Examining modern strategies for effective and sustainable agricultural plant protection techniques: A review. International Journal of Environment and Climate Change 13 (11): 1331-1343. DOI:
https://doi.org/10.9734/ijecc/....
28.
Hunt E. R., Hively W. D., Fujikawa S. J., Linden D.S., Daughtry C.S.T., McCarty G.W. 2010. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing 2 (1): 290–305. DOI:
https://doi.org/10.3390/rs2010....
29.
Jie Z., Wang Q., Song G., Jin J. 2023. Validating and developing hyperspectral indices for tracing leaf chlorophyll fluorescence parameters under varying light conditions. Remote Sensing 15 (19): 4890. DOI:
https://doi.org/10.3390/rs1519....
30.
Jin X., Jie L., Wang S., Qi H. J., Li S. W. 2018. Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sensing 10 (3): 395. DOI:
https://doi.org/10.3390/rs1003....
31.
Karpiński S., Szechyńska-Hebda M. 2023. Systemic acquired acclimation, network acquired acclimation and cellular light memory in plants – Molecular, biochemical, and physiological mechanisms. Advances in Botanical Research 105: 277–310. DOI:
https://doi.org/10.1016/bs.abr....
32.
Khanal S., Fulton J., Shearer S. 2017. An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture 139: 22–32. DOI:
https://doi.org/10.1016/j.comp....
33.
Koshariya A., Sharma N., Satapathy S., Thilagam P., Laxman T., Rai S., Singh B. 2023. Safeguarding agriculture: a comprehensive review of plant protection strategies. International Journal of Environment and Climate Change 13 (11): 272–281. DOI:
https://doi.org/10.9734/ijecc/....
34.
Lancashire P. D., Bleiholder H., van den Boom T., Langelüddeke P., Stauss R., Weber E., Witzenberger A. 1991. A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology 119: 561–601. DOI:
https://doi.org/10.1111/j.1744....
35.
Lowe A., Harrison N., French A. P. 2017. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13 (1): 80. DOI: 10.1186/s13007-017-0233-z.
36.
Lu J., Hu J., Zhao G., Mei F., Zhang C. 2017. An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture 142: 369–379. DOI:
https://doi.org/10.1016/j.comp....
37.
Ma H., Huang W., Dong Y., Liu L., Guo A. 2021. Using UAV-based hyperspectral imagery to detect winter wheat Fusarium Head Blight. Remote Sensing 13 (15): 3024. DOI:
https://doi.org/10.3390/rs1315....
38.
Mahlein A. K., Oerke E. C., Steiner U., Dehne H. W. 2012. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology 133 (1): 197–209. DOI:
https://doi.org/10.1007/s10658....
39.
Mahlein A. K., Rumpf T., Welke P., Dehne H. W., Plümer L., Steiner U., Oerke E. C. 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment 128: 21–30. DOI:
https://doi.org/10.1016/j.rse.....
40.
Mahlein A.-K., Kuska M.T., Behmann J., Polder G., Walter A. 2018. Hyperspectral sensors and imaging technologies in phytopathology: State of the art. Annual Review of Phytopathology 56: 535–558. DOI:
https://doi.org/10.1146/annure....
41.
Monteiro A., Santos S., Gonçalves P. 2021. Precision agriculture for crop and livestock farming – brief review. Animals MDPI 11 (8): 2345. DOI:
https://doi.org/10.3390/ani110....
42.
Mostafalou S., Abdollahi M. 2017. Pesticides and human chronic diseases: Evidences, mechanisms, and perspectives. Toxicology and Applied Pharmacology 324: 94–104. DOI: 10.1016/j.taap.2013.01.025.
43.
Moustaka J., Moustakas M. 2023. Early-stage detection of biotic and abiotic stress on plants by chlorophyll fluorescence imaging analysis. Biosensors 13 (8): 796. DOI:
https://doi.org/10.3390/bios13....
44.
Nguyen C., Sagan V., Maimaitiyiming M., Maimaitijiang M., Bhadra S., Kwasniewski M. T. 2021. Early detection of plant viral disease using hyperspectral imaging and deep learning. Sensors 21 (3): 742. DOI:
https://doi.org/10.3390/s21030....
45.
Pasternak M., Pawłuszek-Filipiak K. 2022. the evaluation of spectral vegetation indexes and redundancy reduction on the accuracy of crop type detection. Applied Sciences 12 (10): 5067. DOI:
https://doi.org/10.3390/app121....
46.
Pena J.M., Torres-Sanchez J., de Castro A.I., Kelly M., Lopez-Granados F. 2013. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 8 (7): e77151. DOI:
https://doi.org/10.1371/journa....
47.
Poobalasubramanian M., Park E., Faqeerzada M., Kim T., Kim M., Baek I., Cho B. 2022. Identification of early heat and water stress in strawberry plants using chlorophyll-fluorescence indices extracted via hyperspectral images. Sensors (Basel, Switzerland) 22: 8706. DOI:
https://doi.org/10.3390/s22228....
48.
Qiu R., Yang C., Moghimi A., Zhang M., Steffenson B. J., Hirsch C. D. 2019. Detection of Fusarium Head Blight in wheat using a deep neural network and color imaging. Remote Sensing 11 (22): 2658. DOI: 10.3390/rs11222658.
50.
Sajid H. 2023. AI development lifecycle: Complete breakdown in 2023. Artificial Intelligence. [Available on:
https://www.unite.ai/ai-develo...] [Accession: April 23, 2023].
51.
Saleem M., Khanchi S., Potgieter J., Arif K. 2020. Image-based plant disease identification by deep learning meta-architectures. Plants 9 (11): 1451. DOI:
https://doi.org/10.3390/plants....
52.
Savary S., Willocquet L., Pethybridge S. J., Esker P., McRoberts N., Nelson A. 2019. The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution 3 (3): 430–439. DOI:
https://doi.org/10.1038/s41559....
53.
Singh V., Sharma N., Singh S. 2020. A review of imaging techniques for plant disease detection. Artificial Intelligence in Agriculture 4: 229–242. DOI:
https://doi.org/10.1016/j.aiia....
54.
Szechyńska-Hebda M., Lewandowska M., Witoń D., Fichman Y., Mittler R., Karpiński S. M. 2022. Aboveground plant-to-plant electrical signaling mediates network acquired acclimation. The Plant Cell 34 (8): 3047–3065. DOI:
https://doi.org/10.1093/plcell....
55.
Szechyńska-Hebda M., Wąsek I., Gołębiowska-Pikania G., Dubas E., Żur I., Wędzony M. 2015. Photosynthesis-dependent physiological and genetic crosstalk between cold acclimation and cold-induced resistance to fungal pathogens in triticale (Triticosecale Wittm.). Journal of Plant Physiology 177: 30–43. DOI: 10.1016/j.jplph.2014.12.017.
56.
Szechyńska-Hebda M., Wędzony M., Tyrka M., Gołębiowska G., Chrupek M., Czyczyło-Mysza I., Dubas E., Zur I., Golemiec E. 2011. Identifying QTLs for cold-induced resistance to Microdochium nivale in winter triticale. Plant Genetic Resources 9 (2): 296–299. DOI:
https://doi.org/10.1017/S14792....
57.
Thenkabail P. S., Smith R. B., De Pauw E. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71 (2): 158–182. DOI:
https://dx.doi.org/10.1016/S00....
58.
Tona E., Calcante A., Oberti R. 2018. The profitability of precision spraying on specialty crops: a technical-economic analysis of protection equipment at increasing technological levels. Precision Agriculture 19: 606–629. DOI:
https://doi.org/10.1007/s11119....
59.
Wan L., Li H., Li C., Wang A., Yang Y., Wang P. 2022. Hyperspectral sensing of plant diseases: principle and methods. Agronomy 12: 1451. DOI:
https://doi.org/10.3390/agrono....
60.
Wąsek I., Dyda M., Gołębiowska G., Tyrka M., Rapacz M., Szechyńska-Hebda M., Wędzony M. 2022. Quantitative trait loci and candidate genes associated with freezing tolerance of winter triticale (× Triticosecale Wittmack). Journal of Applied Genetics 63 (1):15–33. DOI: 10.1007/s13353-021-00660-1.
61.
Wielkopolska Izba Rolnicza. 2024. Calculation of production costs for winter wheat. [Available on:
https://wir.org.pl/asp/start,0] [Accession: June, 2024] (in Polish).
62.
Zaka M.M., Samat A. 2024. Advances in remote sensing and machine learning methods for invasive plants study: a comprehensive review. Remote Sensing 16 (20): 3781. DOI:
https://doi.org/10.3390/rs1620....
63.
Zanin A., Neves D., Pereira L., Ribeiro T., da Silva C., da Silva S., Teodoro P., Rojo Baio T. 2022. Reduction of pesticide application via real-time precision spraying. Scientific Reports 12: 563. DOI:
https://doi.org/10.1038/s41598....
64.
Zhang C., Kovacs J.M. 2012. The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture 13 (6): 693–712. DOI:
https://doi.org/10.1007/s11119....
65.
Zhelezova S., Pakholkova E., Veller V., Voronov M., Stepanova E., Zhelezova A., Sonyushkin A., Zhuk T., Glinushkin A. 2023. Hyperspectral non-imaging measurements and perceptron neural network for pre-harvesting assessment of damage degree caused by Septoria/Stagonospora Blotch Diseases of wheat. Agronomy 13 (4): 1045. DOI:
https://doi.org/10.3390/agrono....