An explainable AI approach to agrotechnical monitoring and crop diseases prediction in Dnipro region of Ukraine

• Autorzy: Laktionov Ivan , Diachenko Grigorii , Rutkowska Danuta , Kisiel-Dorohinicki Marek

Identyfikatory

DOI

10.2478/jaiscr-2023-0018

• Języki publikacji: EN

Abstrakty

EN

The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of highperformance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.

Słowa kluczowe

EN

IoT; ANFIS; explainable AI; agrotechnical monitoring; disease prediction; crop

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2023
• Tom: Vol. 13, No. 4
• Strony: 247--272
• Opis fizyczny: Bibliogr. 52 poz., rys.

Twórcy

autor

Laktionov Ivan

• Dnipro University of Technology, av. Dmytra Yavornytskoho, 19, Dnipro, UA49005, Ukraine

• https://orcid.org/0000-0001-7857-6382

autor

Diachenko Grigorii

• Dnipro University of Technology, av. Dmytra Yavornytskoho, 19, Dnipro, UA49005, Ukraine

• https://orcid.org/0000-0001-9105-1951

autor

Rutkowska Danuta

• University of Social Sciences, 90-113, Łódź, Poland

• https://orcid.org/0000-0003-0217-2589

autor

Kisiel-Dorohinicki Marek

• AGH University in Krakow, 30-059, Kraków, Poland

• https://orcid.org/0000-0002-8459-1877

Bibliografia

• [1] Food and Agriculture Organization of the United Nations: Climate change and food security: risks and responses. Available at: fao.org/3/i5188e/I5188E.pdf [Accessed 14 June 2023].
• [2] R. Lopez-Lozano, B. Baruth, An evaluation framework to build a cost-efficient crop monitoring system. Experiences from the extension of the European crop monitoring system, Agric. Systems, 168, 2019, pp. 231–246. doi.org/10.1016/j.agsy.2018.04.002.
• [3] FAOSTAT: Crop and livestock products. Available at: fao.org/faostat/en/#data/QCL [Accessed 18 June 2023].
• [4] FAO: Agricultural production statistics 2000 – 2021. Available at: fao.org/3/cc3751en/cc3751en.pdf [Accessed 20 June 2023].
• [5] M.F. Mushtaq, U. Akram, M. Aamir, H. Ali, M. Zulqarnain, Neural Network Techniques for Time Series Prediction: A Review, JOIV: International Journal on Informatics Visualization, 3 (3), 2019, pp. 314–320. dx.doi.org/10.30630/joiv.3.3.281.
• [6] E. Funes, Y. Allouche, G. Beltran, A. Jiménez A Review: Artificial Neural Networks as Tool for Control Food Industry Process, Journal of Sensor Tech., 5, 2015, pp. 28–43. doi.org/10.4236/jst.2015.51004.
• [7] K. Suzuki (Ed.), Artificial Neural Networks – Industrial and Control Engineering Applications, InTech, 2011. doi.org/10.5772/2041.
• [8] S. Kujawa, G. Niedbała (Ed.), Artificial Neural Networks in Agriculture, MDPI, 2021. doi.org/10.3390/books978-3-0365-1579-3.
• [9] S. Francik, Z. Slipek, J. Fraczek, A. Knapczyk, Present Trends in Research on Application of Artificial Neural Networks in Agricultural Engineering, Agricultural Engineering, 20 (4), 2017, pp. 15–25. doi.org/10.1515/agriceng-2016-0060.
• [10] L. Rutkowski, Generalized Regression Neural Networks in Time-Varying Environment, IEEE Transactions on Neural Networks, 15 (3), 2004, pp. 576–596. doi.org/10.1109/TNN.2004.826127.
• [11] L. Rutkowski, Adaptive Probabilistic Neural Networks for Pattern Classification in TimeVarying Environment, IEEE Transactions on Neural Networks, 15 (4), 2004, pp. 811–827. doi.org/10.1109/TNN.2004.828757.
• [12] M. Perez-Pons, J. Parra-Dominguez, S. Omatu, E. Herrera-Viedma, J. Corchado, Machine Learning and Traditional Econometric Models: A Systematic Mapping Study, Journal of Artificial Intelligence and Soft Computing Research, 12 (2), 2022, pp. 79–100. doi.org/10.2478/jaiscr-2022-0006.
• [13] J. Lv, M. Pawlak, Bandwidth Selection for Kernel Generalized Regression Neural Networks in Identification of Hammerstein Systems, Journal of Artificial Intelligence and Soft Computing Research, 11 (3), 2021, pp. 181–194. https://doi.org/10.2478/jaiscr-2021-0011.
• [14] T.J. Ross, J.M. Booker, W.J. Parkinson (Eds.), Fuzzy Logic and Probability Applications: Bridging the Cap, ASA-SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, ASA, Alexandria, VA, 2002. URL: pzs.dstu.dp.ua/logic/bibl/prob.pdf.
• [15] X. Li, M.J. Er, B.S. Lim, J.H. Zhou, O.P. Gan, L. Rutkowski, Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection, Int. J. Neural. Syst., 20 (5), 2010, pp. 405–419. doi.org/10.1142/S0129065710002498.
• [16] I. Laktionov, O. Vovna, M. Kabanets, ComputerOriented Method of Adaptive Monitoring and Control of Temperature and Humidity Mode of Greenhouse Production, Baltic J. Modern Computing, 11 (1), 2023, pp. 202–225. doi.org/10.22364/bjmc.2023.11.1.12.
• [17] I. Laktionov, O. Vovna, M. Kabanets, Information Technology for Comprehensive Monitoring and Control of the Microclimate in Industrial Greenhouses Based on Fuzzy Logic, Journal of Artificial Intelligence and Soft Computing Research, 13 (1), 2023, pp. 19–35. doi.org/10.2478/jaiscr2023-0002.
• [18] L. Rutkowski, Flexible Neuro-Fuzzy Systems, Springer New York, NY, 2004. doi.org/10.1007/b115533
• [19] D. Rutkowska, Neuro-Fuzzy Architectures and Hybrid Learning, Physica Heidelberg, 2002. doi.org/10.1007/978-3-7908-1802-4.
• [20] D. Karaboga, E. Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, Artif. Intell. Rev., 52, 2019, pp. 2263–2293. doi.org/10.1007/s10462-017-9610-2.
• [21] P. Dziwinski, A. Przybył, P. Trippner, J. Paszkowski, Y. Hayashi, Hardware Implementation of a Takagi-Sugeno Neuro-Fuzzy System Optimized by a Population Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 11 (3), 2021, pp.243–266. doi.org/10.2478/jaiscr-2021-0015.
• [22] A. Niewiadomski, M. Kacprowicz, Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention, Journal of Artificial Intelligence and Soft Computing Research, 11 (2), 2021, pp. 85–97. doi.org/10.2478/jaiscr-2021-0006.
• [23] Y.K. Al-Douri, H. Hamodi, J. Lundberg, Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans, Algorithms, 11 (123), 2018, pp. 1–19. doi.org/10.3390/a11080123.
• [24] I. Aouadni, A. Rebai, Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction, Ann. Oper. Res., 256, 2017, pp. 3–20. doi.org/10.1007/s10479-016-2154-z.
• [25] H. Han, J. Siebert, TinyML: A Systematic Review and Synthesis of Existing Research, In: 2022 Int. Conf. on Artificial Intelligence in Information and Communication, Jeju Island, Republic of Korea, 2022, pp. 269–274. doi.org/10.1109/ICAIIC54071.2022.9722636.
• [26] P.P. Ray, A review on TinyML: State-of-the-art and prospects, Journal of King Saud University – Computer and Information Sciences, 34 (4), 2022, pp. 1595–1623. doi.org/10.1016/j.jksuci.2021.11.019.
• [27] N. Schizas, A. Karras, C. Karras, S. Sioutas, TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review, Future Internet, 14 (363), 2022, pp. 1–45. doi.org/10.3390/fi14120363.
• [28] G. Schwalbe, B. Finzel, A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts, Data Mining and Knowledge Discovery, 2023, pp. 1–59. doi.org/10.1007/s10618-022-00867-8.
• [29] W. Saeed, C. Omlin, Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities, Knowledge-Based Syst., 263, 2023, pp. 1–24. doi.org/10.1016/j.knosys.2023.110273.
• [30] G. Vilone, L. Longo, Notions of explainability and evaluation approaches for explainable artificial intelligence, Information Fusion, 76, 2021, pp. 89–106. doi.org/10.1016/j.inffus.2021.05.009.
• [31] P. Mishra, Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python, Springer, 2023. doi.org/10.1007/978-1-4842-9029-3.
• [32] M. Ryo, Explainable artificial intelligence and interpretable machine learning for agricultural data analysis, Artif. Intelligence in Agric., 6, 2022, pp. 257–265. doi.org/10.1016/j.aiia.2022.11.003.
• [33] S. Mohr, R. Kuhl, Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior, Precision Agric., 22, 2021, pp. 1816–1844. doi.org/10.1007/s11119-021-09814-x.
• [34] N.L. Tsakiridis, T. Diamantopoulos, A.L. Symeonidis, J.B. Theocharis, A. Iossifides, P. Chatzimisios, G. Pratos, D. Kouvas, Versatile Internet of Things for Agriculture: An eXplainable AI Approach, Artificial Intelligence Applications and Innovations, 584, 2020, pp. 180–191. doi.org/10.1007/978-3-030-49186-4 16.
• [35] I. Ahmed, G. Jeon, F. Piccialli, From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where, IEEE Transactions on Industrial Informatics, 18 (8), 2022, pp. 5031–5042. doi.org/10.1109/TII.2022.3146552.
• [36] S. Rakesh, M. Indiramma, Explainable AI for Crop disease detection, In: 2022 4th Int. Conf. on Advances in Computing, Communication Control and Networking, Greater Noida, India, 2022, pp. 1601–1608. doi.org/10.1109/ICAC3N56670.2022.10074303.
• [37] H. Oubehar, A. Selmani, A. Ed-Dahhak, A. Lachhab, M. El Hassane Archidi, B. Bouchikhi, ANFIS-Based Climate Controller for Computerized Greenhouse System, Adv. Sci. Technol. Eng. Syst. J., 5 (1), 2020, pp. 8–12. doi.org/10.25046/aj050102.
• [38] V. Vincentdo, N. Surantha, Nutrient Film Technique-Based Hydroponic Monitoring and Controlling System Using ANFIS, Electronics, 12 (1446), 2023, pp. 1–26. doi.org/10.3390/electronics12061446.
• [39] D. Bozanic, D. Tesic, D. Marinkovic, A. Milic, Modeling of neuro-fuzzy system as a suport in decision-making processes, Reports in Mechanical Engineering, 2 (1), 2021, pp. 222–234. doi.org/10.31181/rme2001021222b.
• [40] N. Ziasabounchi, I.N. Askerzade, ANFIS Based Classification Model for Heart Disease Prediction, Int. J. of Engineering & Computer Science IJECS-IJENS, 14 (02), 2014, pp. 7–12, URL: ijens.org/IJECSVol14Issue02.html.
• [41] S. Bellahirich, D. Mezghani, A. Mami, Design and Implementation of an Intelligent ANFIS Controller on a Raspberry Pi Nano-Computer for Photovoltaic Pumping Intended for Drip Irrigation, Energies, 14 (5217), 2021, pp. 1–19. doi.org/10.3390/en14175217.
• [42] A.S. Keceli, A. Kaya, C. Catal, B. Tekinerdogan, Deep learning-based multi-task prediction system for plant disease and species detection, Ecological Informatics, 69, 2022, pp. 1–14. doi.org/10.1016/j.ecoinf.2022.101679.
• [43] S.H. Lee, H. Goeau, P. Bonnet, A. Joly, Conditional Multi-Task learning for Plant Disease Identification, In: 2020 25th Int. Conf. on Pattern Recognition, Milan, Italy, 2021, pp. 3320–3327. doi.org/10.1109/ICPR48806.2021.9412643.
• [44] M.S. Hema, N. Sharma, Y. Sowjanya, Ch. Santoshini, R. Sri Durga, V. Akhila, Plant disease prediction using convolutional neural network, EMITTER Int. J. of Engineering Technology, 9 (2), 2021, pp. 283–293. doi.org/10.24003/emitter.v9i2.640.
• [45] T.V. Reddy, K.S. Rekha, Plant Disease Detection Using Advanced Convolutional Neural Networks with Region of Interest Awareness, Stem Cell Res Int, 6 (2), 2022, pp. 121–131. doi.org/10.47363/JIRR/2022(2)117.
• [46] T. Domingues, T. Brandao, J.C. Ferreira, Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey, Agriculture, 12 (1350), 2022, pp. 1–23. doi.org/10.3390/agriculture12091350.
• [47] H. Bangui, S. Rakrak, S. Raghay, B. Buhnova, Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions, Electronics, 7 (11:309), 2018, pp. 1–31. doi.org/10.3390/electronics7110309.
• [48] I.S. Laktionov, O.V. Vovna, M.M. Kabanets, H.O. Sheina, I.A. Getman, Information model of the computer-integrated technology for wireless monitoring of the state of microclimate of industrial agricultural greenhouses, Instrumentation Mesure Metrologie, 20 (6), 2021, pp. 289–300. doi.org/10.18280/i2m.200601.
• [49] I. Laktionov, L. Rutkowski, O. Vovna, A. Byrski, M. Kabanets, A Novel Approach to Intelligent Monitoring of Gas Composition and Light Mode of Greenhouse Crop Growing Zone on the Basis of Fuzzy Modelling and Human-in-the-Loop Techniques, Engineering Applications of AI, 126 (B), 2023, pp. 1–21, doi.org/10.1016/j.engappai.2023.106938.
• [50] Crop Science Australia: Fusarium head blight. Available at: crop.bayer.com.au/pests/diseases/fusariumhead-blight [Accessed 25 July 2023].
• [51] University of Illinois: Report on plant diseases. Common leaf blights and spots of corn. Available at: ipm.illinois.edu/diseases/rpds/202.pdf [Accessed 25 July 2023].
• [52] MathWorks: Nonlinear System Identification. Available at: mathworks.com/help/fuzzy/nonlinear-systemidentification.html [Accessed 30 July 2023].

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).