Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico

Contenido principal del artículo

Ana Cristina Umaquinga Criollo

Resumen

En esta investigación, se presenta un análisis bibliométrico de 106 artículos de revistas y estado del arte indexados en Scopus, junto con un análisis sistemático de 83 artículos seleccionados. Se identifican áreas de estudio que incluye la predicción de rendimiento y crecimiento de cultivos, la detección de enfermedades en plantas, análisis de agua y suelo, relacionados con diferentes tipos de cultivo como: cereales (arroz, cebada, maíz, trigo, soya); frutas (manzana, pepino); legumbres (alfalfa, frejol, cacahuate); tubérculos, entre otros. Se examinan variables climáticas, suelo, agua, condiciones topográficas, edafológicas y técnicas de minería de datos como, Redes Neuronales, Deep Learning, segmentación, reglas de asociación y clasificación, entre otras, para optimizar el uso de recursos y tomar decisiones agrícolas basadas en datos. Además, se destacan los desafíos y oportunidades en esta área de investigación, así como las perspectivas futuras para el desarrollo de soluciones de minería de datos avanzadas en el contexto agrícola. Este análisis contribuye a una mejor comprensión de cómo la minería de datos está transformando el sector agrícola, comunidad académica y científica, con el fin de impulsar la eficiencia, la sostenibilidad y la toma de decisiones informadas en la producción de alimentos.

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Umaquinga Criollo, A. C. (2024). Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico. INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES, 6(1), 25. https://doi.org/10.53358/ideas.v6i1.944
Sección
Information and Electronic Engineering

Citas

Choudhury, A., Biswas, A., Prateek, M., Chakrabarti, A.: Agricultural Informatics: Automation Using the IoT and Machine Learning. wiley (2021). https://doi.org/10.1002/9781119769231.

Castillejo, P., Johansen, G., Cürüklü, B., Bilbao-Arechabala, S., Fresco, R., Martínez-Rodríguez, B., Pomante, L., Rusu, C., Martínez-Ortega, J.-F., Centofanti, C., Hakojärvi, M., Santic, M., Häggman, J.: Aggregate Farming in the Cloud: The AFarCloud ECSEL project. Microprocess. Microsyst. 78, (2020). https://doi.org/10.1016/j.micpro.2020.103218.

Kavitha, G., Elango, N.M.: An overview of data mining techniques and its applications. Int. J. Civ. Eng. Technol. 8, 1013–1020 (2017).

Kommineni, M., Perla, S., Yedla, D.B.: A survey of using data mining techniques for soil fertility. Int. J. Eng. Technol. 7, 917–918 (2018).

Belinda, M.J.C.M., Umamaheswari, R., David, S.A.: Study of high yielding crops cultivation in India using data mining techniques. Int. J. Eng. Technol. 7, 121–124 (2018).

Pandiyaraju, V., Logambigai, R., Ganapathy, S., Kannan, A.: An Energy Efficient Routing Algorithm for WSNs Using Intelligent Fuzzy Rules in Precision Agriculture. Wirel. Pers. Commun. 112, 243–259 (2020). https://doi.org/10.1007/s11277-020-07024-8.

Majumdar, J., Naraseeyappa, S., Ankalaki, S.: Analysis of agriculture data using data mining techniques: application of big data. J. Big Data. 4, (2017). https://doi.org/10.1186/s40537-017-0077-4.

Vandna, Bansal, K.L.: Data mining techniques for increasing smart farming in agrarian sector. Int. J. Sci. Technol. Res. 9, 3555–3562 (2020).

Xu, W., Wang, Q., Chen, R.: Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks. Geoinformatica. 22, 363–381 (2018). https://doi.org/10.1007/s10707-017-0314-1.

de Lima, M.D., Barbosa, R.: Methods of authentication of food grown in organic and conventional systems using chemometrics and data mining algorithms: A review. Food Anal. Methods. 12, 887–901 (2019). https://doi.org/10.1007/s12161-018-01413-3.

Organización de las Naciones Unidas para la Alimentación y la Agricultura: Ecuador en una mirada | FAO en Ecuador | Organización de las Naciones Unidas para la Alimentación y la Agricultura, https://www.fao.org/ecuador/fao-en-ecuador/ecuador-en-una-mirada/es/, last accessed 2022/10/02.

Cutamora, J.C., Padua, R.: An Economic Rationalization Framework for Higher Education. Recoletos Multidiscip. Res. J. 8, 43–65 (2020). https://doi.org/10.32871/rmrj2008.01.04.

Ait Issad, H., Aoudjit, R., Rodrigues, J.J.P.C.: A comprehensive review of Data Mining techniques in smart agriculture. Eng. Agric. Environ. Food. 12, 511–525 (2019). https://doi.org/10.1016/j.eaef.2019.11.003.

Sunhare, P., Chowdhary, R.R., Chattopadhyay, M.K.: Internet of things and data mining: An application oriented survey. J. King Saud Univ. - Comput. Inf. Sci. 34, 3569–3590 (2022). https://doi.org/10.1016/j.jksuci.2020.07.002.

Aria, M., Cuccurullo, C.: bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 11, 959–975 (2017). https://doi.org/10.1016/J.JOI.2017.08.007.

Computers and Electronics in Agriculture | Journal | ScienceDirect.com by Elsevier, https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture, last accessed 2023/09/09.

Computers and Electronics in Agriculture, https://www.scimagojr.com/journalsearch.php?q=30441&tip=sid&clean=0, last accessed 2023/09/09.

Scopus - Computers and Electronics in Agriculture | Signed in, https://www.scopus.com/sourceid/30441, last accessed 2023/09/09.

International Journal of Innovative Technology and Exploring Engineering, https://www.scimagojr.com/journalsearch.php?q=21100889409&tip=sid&clean=0, last accessed 2023/09/11.

Harzing, A.W., Alakangas, S.: LibGuides: Impact Metrics: Author Impact: h-index, g-index... Scientometrics. 106, 787–804 (2016). https://doi.org/10.1007/S11192-015-1798-9.

Nasrnia, F., Ashktorab, N.: Sustainable livelihood framework-based assessment of drought resilience patterns of rural households of Bakhtegan basin, Iran. Ecol. Indic. 128, (2021). https://doi.org/10.1016/j.ecolind.2021.107817.

Li, S., Bhattarai, R., Cooke, R.A., Verma, S., Huang, X., Markus, M., Christianson, L.: Relative performance of different data mining techniques for nitrate concentration and load estimation in different type of watersheds. Environ. Pollut. 263, (2020). https://doi.org/10.1016/j.envpol.2020.114618.

Kumar, Y.J.N., Kanth, T.V.R.: GIS-MAP based spatial analysis of rainfall data of Andhra Pradesh and telangana states using R. Int. J. Electr. Comput. Eng. 7, 460–468 (2017). https://doi.org/10.11591/ijece.v7i1.pp460-468.

Othman, Z.A., Ismail, N., Hamdan, A.R., Sammour, M.A.: Klang vally rainfall forecasting model using time series data mining technique. J. Theor. Appl. Inf. Technol. 92, 372–379 (2016).

Refonaa, J., Lakshmi, M.: Accurate prediction of the rainfall using convolutional neural network and parameters optimization using improved particle swarm optimization. J. Adv. Res. Dyn. Control Syst. 11, 318–328 (2019).

Hsiou, D.-C., Huang, F., Tey, F.J., Wu, T.-Y., Lee, Y.-C.: An Automated Crop Growth Detection Method Using Satellite Imagery Data. Agric. 12, (2022). https://doi.org/10.3390/agriculture12040504.

Mohammed, S., Elbeltagi, A., Bashir, B., Alsafadi, K., Alsilibe, F., Alsalman, A., Zeraatpisheh, M., Széles, A., Harsányi, E.: A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean. Comput. Electron. Agric. 197, (2022). https://doi.org/10.1016/j.compag.2022.106925.

Sheik Mohideen Shah, S., Meganathan, S., Kamali, A.: Soft computing research for weather prediction using multilayer architecture. Int. J. Eng. Adv. Technol. 8, 3779–3783 (2019). https://doi.org/10.35940/ijeat.F9390.088619.

Vinoth, B., Elango, N.M.: An effective data mining techniques based optimal paddy yield cultivation: a rational approach. Paddy Water Environ. 19, 331–343 (2021). https://doi.org/10.1007/s10333-021-00845-8.

Marshall, M., Thenkabail, P.: Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS J. Photogramm. Remote Sens. 108, 205–218 (2015). https://doi.org/10.1016/j.isprsjprs.2015.08.001.

Pineda, M., Barón, M., Pérez-Bueno, M.-L.: Thermal imaging for plant stress detection and phenotyping. Remote Sens. 13, 1–21 (2021). https://doi.org/10.3390/rs13010068.

Delerce, S., Dorado, H., Grillon, A., Rebolledo, M.C., Prager, S.D., Patiño, V.H., Varón, G.G., Jiménez, D.: Assessing weather-yield relationships in rice at local scale using data mining approaches. PLoS One. 11, (2016). https://doi.org/10.1371/journal.pone.0161620.

Arumugam, A.: A predictive modeling approach for improving paddy crop productivity using data mining techniques. Turkish J. Electr. Eng. Comput. Sci. 25, 4777–4787 (2017). https://doi.org/10.3906/elk-1612-361.

Ahmed, G.N., Kamalakannan, S., Kavitha, P.: A Machine Learning Approach for Stochastic Pattern Analysis for the Measurement of Time-Series Datasets. Instrum. Mes. Metrol. 21, 199–205 (2022). https://doi.org/10.18280/i2m.210505.

Gholizadeh, A., Carmon, N., Klement, A., Ben-Dor, E., Borůvka, L.: Agricultural soil spectral response and properties assessment: Effects of measurement protocol and data mining technique. Remote Sens. 9, (2017). https://doi.org/10.3390/rs9101078.

Farhate, C.V. V, De Souza, Z.M., De Medeiros Oliveira, S.R., Tavares, R.L.M., Carvalho, J.L.N.: Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field. PLoS One. 13, (2018). https://doi.org/10.1371/journal.pone.0193537.

Radhika, A., Masood, M.S.: Effective dimensionality reduction by using soft computing method in data mining techniques. Soft Comput. 25, 4643–4651 (2021). https://doi.org/10.1007/s00500-020-05474-7.

Kiruthika, V.G., Arutchudar, V., Senthil Kumar, P.: Highest humidity prediction using data mining techniques. Int. J. Appl. Eng. Res. 9, 3259–3264 (2014).

Uchimiya, M., Bannon, D., Nakanishi, H., McBride, M.B., Williams, M.A., Yoshihara, T.: Chemical Speciation, Plant Uptake, and Toxicity of Heavy Metals in Agricultural Soils. J. Agric. Food Chem. 68, 12856–12869 (2020). https://doi.org/10.1021/acs.jafc.0c00183.

Prathik, A., Anuradha, J., Uma, K.: A novel algorithm for soil image segmentation using color and region based system. Int. J. Innov. Technol. Explor. Eng. 8, 3544–3550 (2019). https://doi.org/10.35940/ijitee.J9762.0881019.

Sumathi, M.S., Anitha, G.S.: Energy efficient wireless sensor network with efficient data handling for real time landslide monitoring system using fuzzy data mining technique. Int. J. Mob. Netw. Des. Innov. 8, 179–193 (2018). https://doi.org/10.1504/IJMNDI.2018.093701.

Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., Uwamahoro, A.: Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize. Agric. 13, (2023). https://doi.org/10.3390/agriculture13010225.

Calçada, D.B., Rezende, S.O., Teodoro, M.S.: Analysis of green manure decomposition parameters in northeast Brazil using association rule networks. Comput. Electron. Agric. 159, 34–41 (2019). https://doi.org/10.1016/j.compag.2019.02.013.

Chandak, P.P., Agrawal, A.J.: Smart farming system using data mining. Int. J. Appl. Eng. Res. 12, 2788–2791 (2017).

Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., Kersting, K.: Metro maps of plant disease dynamics-automated mining of differences using hyperspectral images. PLoS One. 10, (2015). https://doi.org/10.1371/journal.pone.0116902.

Tripathy, A.K., Adinarayana, J., Vijayalakshmi, K., Merchant, S.N., Desai, U.B., Ninomiya, S., Hirafuji, M., Kiura, T.: Knowledge discovery and Leaf Spot dynamics of groundnut crop through wireless sensor network and data mining techniques. Comput. Electron. Agric. 107, 104–114 (2014). https://doi.org/10.1016/j.compag.2014.05.009.

Krishna Priya, C.B., Venkateswari, S.: Analysis of different clustering algorithms on management zones in precision agriculture. J. Adv. Res. Dyn. Control Syst. 11, 489–494 (2019).

Fernández, R., Montes, H., Surdilovic, J., Surdilovic, D., Gonzalez-De-Santos, P., Armada, M.: Automatic detection of field-grown cucumbers for robotic harvesting. IEEE Access. 6, 35512–35526 (2018). https://doi.org/10.1109/ACCESS.2018.2851376.

Martín, J., Sáez, J.A., Corchado, E.: On the suitability of stacking-based ensembles in smart agriculture for evapotranspiration prediction. Appl. Soft Comput. 108, (2021). https://doi.org/10.1016/j.asoc.2021.107509.

Yu, H., Kong, B., Hou, Y., Xu, X., Chen, T., Liu, X.: A critical review on applications of hyperspectral remote sensing in crop monitoring. Exp. Agric. 58, (2022). https://doi.org/10.1017/S0014479722000278.

Sanches, G.M., Graziano Magalhães, P.S., Junqueira Franco, H.C.: Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma. 334, 90–98 (2019). https://doi.org/10.1016/j.geoderma.2018.07.051.

Saritha, S., Abel Thangaraja, G.: CROP YIELD PREDICTION IN BIG DATA USING MARGALEF KERNEL PERCEPTRON BASED WINNOW BROWN BOOST CLASSIFIER. J. Theor. Appl. Inf. Technol. 101, 2091–2107 (2023).

Geetha, M.C.S., Elizabeth Shanthi, I.: Forecasting the crop yield production in trichy district using fuzzy C-Means Algorithm and Multilayer Perceptron (MLP). Int. J. Knowl. Syst. Sci. 11, 83–98 (2020). https://doi.org/10.4018/IJKSS.2020070105.

Maury, R.K., Yadav, S.K., Sharma, T.K.: Estimation of major agricultural crop with effective yield prediction using data mining. Int. J. Innov. Technol. Explor. Eng. 8, 170–174 (2019).

Chiche, A.: Hybrid decision support system framework for crop yield prediction and recommendation. Int. J. Comput. 18, 181–190 (2019).

Adil, N., Dewangan, S., Sharma, K.: Efficient classification and regression techniques to predict crop yield. Int. J. Sci. Technol. Res. 8, 378–382 (2019).

Blazquez, D., Domenech, J., Garcia-Alvarez-Coque, J.-M.: Assessing technology platforms for sustainability withweb data mining techniques. Sustain. 10, (2018). https://doi.org/10.3390/su10124497.

Patil, N.N., Saiyyad, M.A.M.: Machine learning technique for crop recommendation in agriculture sector. Int. J. Eng. Adv. Technol. 9, 1359–1363 (2019). https://doi.org/10.35940/ijeat.A1171.109119.

Sivanantham, V., Sangeetha, V., Alnuaim, A.A., Hatamleh, W.A., Anilkumar, C., Hatamleh, A.A., Sweidan, D.: Quantile correlative deep feedforward multilayer perceptron for crop yield prediction. Comput. Electr. Eng. 98, (2022). https://doi.org/10.1016/j.compeleceng.2022.107696.

de Barros, F.M.M., Oliveira, S.R.M., de Oliveira, L.H.M.: Development and validation of a recommender system for technologial information on sugarcane [Desenvolvimento e validação de um sistema de recomendação de informações tecnológicas sobre cana-de-açúcar]. Bragantia. 72, 387–395 (2013). https://doi.org/10.1590/brag.2013.049.

Kaviarasan, S., Vanitha, M.: E-farming management system using data mining techniques. Int. J. Intell. Unmanned Syst. 10, 257–266 (2022). https://doi.org/10.1108/IJIUS-05-2020-0018.

Warren Raffa, D., Bogdanski, A., Tittonell, P.: How does crop residue removal affect soil organic carbon and yield? A hierarchical analysis of management and environmental factors. Biomass and Bioenergy. 81, 345–355 (2015). https://doi.org/10.1016/j.biombioe.2015.07.022.

Revathy, R., Balamurali, S., Lawrance, R.: Classifying agricultural crop pest data using hadoop MapReduce based C5.0 algorithm. J. Cyber Secur. Mobil. 8, 393–408 (2019). https://doi.org/10.13052/jcsm2245-1439.835.

Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.-X., Chen, W.: Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci. Total Environ. 625, 575–588 (2018). https://doi.org/10.1016/j.scitotenv.2017.12.256.

Godara, S., Toshniwal, D.: Deep Learning-based query-count forecasting using farmers’ helpline data. Comput. Electron. Agric. 196, (2022). https://doi.org/10.1016/j.compag.2022.106875.

Suarez, A.J.B., Singh, B., Almukhtar, F.H., Kler, R., Vyas, S., Kaliyaperumal, K.: Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques. J. Food Qual. 2022, (2022). https://doi.org/10.1155/2022/6600049.

Bach, M.P., Vlahović, N., Pivar, J.: Fraud Prevention in the Leasing Industry Using the Kohonen Self-Organising Maps. Organizacija. 53, 128–145 (2020). https://doi.org/10.2478/orga-2020-0009.

Ngabalin, A.M.: An investigation on value chain cooperation attributes in fisheries micro-enterprises. Accounting. 6, 301–306 (2020). https://doi.org/10.5267/j.ac.2020.2.005.

Iantovics, L.B., Rotar, C., Morar, F.: Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9, (2019). https://doi.org/10.1002/widm.1294.

Beniwal, S., Das, B.: Mushroom classification using data mining techniques. Int. J. Pharma Bio Sci. 6, B1170–B1176 (2015).

Correa, F.E., Oliveira, M.D.B., Gama, J., Corrêa, P.L.P., Rady, J.: Analyzing the behavior dynamics of grain price indexes using Tucker tensor decomposition and spatio-temporal trajectories. Comput. Electron. Agric. 120, 72–78 (2016). https://doi.org/10.1016/j.compag.2015.11.011.

Shortridge, J.E., Falconi, S.M., Zaitchik, B.F., Guikema, S.D.: Climate, agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques. J. Appl. Stat. 42, 2367–2390 (2015). https://doi.org/10.1080/02664763.2015.1032216.

Sun, L., Zheng, Z., Zhu, J.: Mining spatio-temporal knowledge of climate for dendrobium officinale in greenhouse cultivation. Recent Adv. Electr. Electron. Eng. 11, 160–166 (2017). https://doi.org/10.2174/2352096510666170921162448.

Shastry, K.A., Sanjay, H.A., Deexith, G.: Quadratic-radial-basis-function-kernel for classifying multi-class agricultural datasets with continuous attributes. Appl. Soft Comput. J. 58, 65–74 (2017). https://doi.org/10.1016/j.asoc.2017.04.049.

Rajagopal, M., Ponnuchamy, M., Kapoor, A.: Water management for irrigation scheduling by computing evapotranspiration using ANFIS modelling. Desalin. Water Treat. 251, 123–133 (2022). https://doi.org/10.5004/dwt.2022.28290.

Demir, B., Gurbuz, F., Eski, I., Kus, Z.A., Yilmaz, K.U., Ercisli, S.: Possible Use of Data Mining for Analysis and Prediction of Apple Physical Properties [Die Anwendung statistischer Methoden (Data-Mining) zur Analyse und Prognose physikalischer Eigenschaften bei Apfel]. Erwerbs-Obstbau. 60, 1–7 (2018). https://doi.org/10.1007/s10341-017-0330-1.

Ullah, A., Mohd Nawi, N., Arifianto, A., Ahmed, I., Aamir, M., Khan, S.N.: Real-time wheat classification system for selective herbicides using broad wheat estimation in deep neural network. Int. J. Adv. Sci. Eng. Inf. Technol. 9, 153–158 (2019). https://doi.org/10.18517/ijaseit.9.1.5031.

Rahman, N.A.B.A., Tan, K.L., Lim, C.K.: Supervised and unsupervised learning in data mining for employment prediction of fresh graduate students. J. Telecommun. Electron. Comput. Eng. 9, 155–161 (2017).

Mousavizadegan, M., Mohabatkar, H.: An evaluation on different machine learning algorithms for classification and prediction of antifungal peptides. Med. Chem. (Los. Angeles). 12, 795–800 (2016). https://doi.org/10.2174/1573406412666160229150823.

Krause, P.J., Bokinala, V.: A tutorial on data mining for Bayesian networks, with a specific focus on IoT for agriculture. Internet of Things (Netherlands). 22, (2023). https://doi.org/10.1016/j.iot.2023.100738.

Shivhare, N., Rahul, A.K., Dwivedi, S.B., Dikshit, P.K.S.: ARIMA based daily weather forecasting tool: A case study for Varanasi. Mausam. 70, 133–140 (2019).

Artículos más leídos del mismo autor/a

Artículos similares

<< < 1 2 3 4 

También puede Iniciar una búsqueda de similitud avanzada para este artículo.