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Machine Algorithm-Based Web Prototype for Crop Pest Detection

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 405)

Abstract

Agriculture is an essential activity because it provides food, raw materials, and employment. This activity is affected by the emergence of pests at any stage of the crop life cycle. In turn, it causes a phytosanitary problem that causes losses in the agricultural production and affects the quality of the final product. Faced with these challenges, this research develops a machine algorithm-based web prototype for crop pest detection. This study focuses on pests of four crops of Ecuadorian highlands, namely: potato, corn, tomato, and apple. The proposed solution is based on machine learning algorithm that best suits our case study. For this, data mining phases, image processing techniques and model evaluation metrics are used. With these requirements, a web prototype is designed and development. Thus, this research provides a computer tool to receive and validate information about the causes and pest treatment. Conclusions and future research are described at the final section of the document.

Keywords

  • Machine learning
  • Artificial intelligence
  • Pest
  • Data mining

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References

  1. The World Bank Group. Agriculture, forestry, and fishing, value added (% of GDP). https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS

  2. Schröer, C., Kruse, F., Marx, J., Kruse, F., Marx, J.: A systematic literature review on applying process model on applying CRISP-DM process model. Procedia Comput. Sci. 181, 526–534 (2021). https://doi.org/10.1016/j.procs.2021.01.199

    CrossRef  Google Scholar 

  3. Moorthy, G., Karthikumar, S., Thuy, N., Chi, L.: Versatile image processing technique for fuel science: a review. Sci. Total Environ (2021). https://doi.org/10.1016/j.scitotenv.2021.146469

    CrossRef  Google Scholar 

  4. Westreich, D., Lessler, J., Funk, M.J.: Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J. Clin. Epidemiol. 63, 826–833 (2010). https://doi.org/10.1016/j.jclinepi.2009.11.020

    CrossRef  Google Scholar 

  5. Antoniadis, A., Lambert-Lacroix, S., Poggi, J.M.: Random forests for global sensitivity analysis: a selective review. Reliab. Eng. Syst. Saf. 206, 107312 (2021). https://doi.org/10.1016/j.ress.2020.107312

    CrossRef  Google Scholar 

  6. Fávero, L.P., Belfiore, P.: Binary and multinomial logistic regression models. In: Fávero, L.P., Belfiore, P. (eds.) Data Science for Business and Decision Making, Chapter 14, pp. 539–615. Academic Press, New York (2019)

    Google Scholar 

  7. Luque, A., Carrasco, A., Martín, A., de las Heras, A.: The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recogn. 91, 216–231 (2019). https://doi.org/10.1016/j.patcog.2019.02.023

  8. Arun Pandian, J., Geetharamani, G.: Data for: identification of plant leaf diseases using a 9-layer deep convolutional neural network. Mendeley Data. 1 (2019). https://doi.org/10.17632/tywbtsjrjv.1

  9. Abbassi, N., Helaly, R., Hajjaji, M.A., Mtibaa, A.: A deep learning facial emotion classification system: a VGGNet-19 based approach. In: Proceedings - STA 2020: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, pp. 271–276. Monastir, Tunisia (2020)

    Google Scholar 

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Correspondence to Julio C. Mendoza-Tello .

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Columba-Guanoluisa, A., Aimacaña-Chuquimarca, J., Rosas-Lara, M., Mendoza-Tello, J.C. (2022). Machine Algorithm-Based Web Prototype for Crop Pest Detection. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A., Durakovic, B. (eds) Emerging Research in Intelligent Systems. CIT 2021. Lecture Notes in Networks and Systems, vol 405. Springer, Cham. https://doi.org/10.1007/978-3-030-96043-8_4

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