Neural Network-Guided Smart Trap for Selective Monitoring of Nocturnal Pest Insects in Agriculture

Joel Hinojosa-Dávalos, Miguel Ángel Robles-García, Melesio Gutiérrez-Lomelí, Ariadna Berenice Flores Jiménez and Cuauhtémoc Acosta Lúa ()
Additional contact information
Joel Hinojosa-Dávalos: Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico
Miguel Ángel Robles-García: Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico
Melesio Gutiérrez-Lomelí: Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico
Ariadna Berenice Flores Jiménez: Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico
Cuauhtémoc Acosta Lúa: Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Jalisco, Mexico

Agriculture, 2025, vol. 15, issue 14, 1-24

Abstract: Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana , Synchlora spp., Estigmene acrea , Sphingomorpha chlorea , Gymnoscelis rufifasciata , and Musca domestica , while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata , and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects.

Keywords: automated insect trap; neural networks; pest monitoring; Lepidoptera; Diptera; precision agriculture; embedded control; sensor integration (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/14/1562/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/14/1562/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:14:p:1562-:d:1706352

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().