Abstract
Conventional plant disease detection approaches are time consuming and require high skills. Above all, it cannot be scaled down to smallholder farmers in most developing countries. Using low cost IoT sensor technologies that are gas, ultrasound and NPK sensors mounted next to maize varieties for profiling these parameters on a given period. Here we report an experiment performed under controlled environment to learn metabolic and pathologic behavioral patterns on healthy and NLB inoculated maize plants by generating time series dataset on profiled Volatile Organic Compounds (VOC), Ultrasound and Nitrogen, Phosphorus, Potassium (NPK). Dataset has been preprocessed with pandas and analyzed using machine learning models which are dickey fuller test and python additive statsmodel and visualized using matplotlib library to enable the inference of an occurrence of a disease a few days post inoculation without subjecting a plant to an invasive procedure. This enabled a deployment and implementation of noninvasive plant disease detection prior to visual symptoms that can be applied on other plants. With analyzed data, the IoT technology in this experiment has enabled the detection of NLB disease on maize disease within seven days post inoculation because of monitoring VOC and ultrasound emission.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Strange, R.N., Scott, P.R.: Plant disease: a threat to global food security. Annu. Rev. Phytopathol. 43, 83–116 (2005). https://doi.org/10.1146/annurev.phyto.43.113004.133839
Wangai, A.W., et al.: First report of maize chlorotic mottle virus and maize lethal necrosis in Kenya. Plant Dis. 96(10), 1582 (2012). https://doi.org/10.1094/PDIS-06-12-0576-PDN
National Agricultural Research Organization (NARO). Pests and diseases management in maize (2011). https://teca.apps.fao.org/teca/fr/technologies/7019. Accessed 18 July 2022
Li, Z., et al.: Real-time monitoring of plant stresses via chemiresistive profiling of leaf volatiles by a wearable sensor. Matter 4(7), 2553–2570 (2021). https://doi.org/10.1016/j.matt.2021.06.009
Hussain, S., Lees, A.K., Duncan, J.M., Cooke, D.E.L.: Development of a species-specific and sensitive detection assay for Phytophthora infestans and its application for monitoring of inoculum in tubers and soil. Plant Pathol. 54(3), 373–382 (2005). https://doi.org/10.1111/j.1365-3059.2005.01175.x
Balodi, R., Bisht, S., Ghatak, A., Rao, K.H.: Plant disease diagnosis: technological advancements and challenges. Indian Phytopathol. 70(3), 275–281 (2017). https://doi.org/10.24838/ip.2017.v70.i3.72487
Li, Z., et al.: Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles. Nat. Plants 5(8), 856–866 (2019). https://doi.org/10.1038/s41477-019-0476-y
Skoczek, A., Piesik, D., Wenda-Piesik, A., Buszewski, B., Bocianowski, J., Wawrzyniak, M.: Volatile organic compounds released by maize following herbivory or insect extract application and communication between plants. J. Appl. Entomol. 141(8), 630–643 (2017). https://doi.org/10.1111/jen.12367
Gagliano, M., Mancuso, S., Robert, D.: Towards understanding plant bioacoustics. Trends Plant Sci. 17(6), 323–325 (2012). https://doi.org/10.1016/j.tplants.2012.03.002
Khait, I., et al.: Plants emit informative airborne sounds under stress. https://doi.org/10.1101/507590
PSU Noisequest. https://www.noisequest.psu.edu/noisebasics.html. Accessed 01 Nov 2022
Downer, J.: Effect of fertilizers on plant diseases - topics in subtropics - ANR blogs. Topics in Subtropics (2013) https://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=12364. Accessed 18 Oct 2022
Bucheyeki, T.L., Tongoona, P., Derera, J., Msolla, S.N.: Combining ability analysis for northern leaf blight disease resistance on Tanzania adapted inbred maize lines. In: Advances in Crop Science and Technology, vol. 05, no. 02 (2017). https://doi.org/10.4172/2329-8863.1000266
Jackson, T.: Northern corn leaf blight, Nebraska Extension (2015)
Onwunali, M.R.O., Mabagala, R.B.: Assessment of yield loss due to northern leaf blight in five maize varieties grown in Tanzania. J. Yeast Fungal Res. 11(1), 37–44 (2020). https://doi.org/10.5897/jyfr2017.0181
Fry, W.E., et al.: The 2009 late blight pandemic in the eastern United States - causes and results. Plant Dis. 97(3), 296–306 (2013). https://doi.org/10.1094/PDIS-08-12-0791-FE
Ge, L., Mu, X., Tian, G., Huang, Q., Ahmed, J., Hu, Z.: Current applications of gas sensor based on 2-D nanomaterial: a mini review. Front. Chem. 7 (2019). https://doi.org/10.3389/fchem.2019.00839
Aditya Satrio, C.B., Darmawan, W., Nadia, B.U., Hanafiah, N.: Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Comput. Sci. 179, 524–532 (2021). https://doi.org/10.1016/j.procs.2021.01.036
8.1 Stationarity and differencing | Forecasting: Principles and Practice, 2nd edn. https://otexts.com/fpp2/stationarity.html. Accessed 18 Nov 2022
Moreno-Torres, J.G., Raeder, T., Alaiz-RodrÃguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern Recognit. 45(1), 521–530 (2012). https://doi.org/10.1016/j.patcog.2011.06.019
Introduction—statsmodels. https://www.statsmodels.org/stable/index.html. Accessed 21 Nov 21
Acknowledgements
This work is financially supported by The PASET Regional Scholarship and Innovation Funds as a part of PhD work scholarship and as well hosted at the African Centre of Excellence in Internet of Things Rwanda. Experimental works have been hosted by Sokoine University of Agriculture, Tanzania.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Maginga, T.J., Massawe, D.P., Kanyagha, H.E., Nahson, J., Nsenga, J. (2023). On Sensing Non-visual Symptoms of Northern Leaf Blight Inoculated Maize for Early Disease Detection Using IoT/AI. In: Czarnowski, I., Howlett, R., Jain, L.C. (eds) Intelligent Decision Technologies. KESIDT 2023. Smart Innovation, Systems and Technologies, vol 352. Springer, Singapore. https://doi.org/10.1007/978-981-99-2969-6_8
Download citation
DOI: https://doi.org/10.1007/978-981-99-2969-6_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2968-9
Online ISBN: 978-981-99-2969-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)