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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)


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.


  • Machine learning
  • Artificial intelligence
  • Pest
  • Data mining

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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.

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