Journal of Agricultural Engineering (Jun 2022)

An ıntelligent system for detecting Mediterranean fruit fly [Medfly; Ceratitis Capitata (Wiedemann)]

  • Yusuf Uzun,
  • Mehmet Resit Tolun,
  • Halil Tanyer Eyyuboglu,
  • Filiz Sarı

DOI
https://doi.org/10.4081/jae.2022.1381

Abstract

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Nowadays, the most critical agriculture-related problem is the harm caused in fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named in short as Medfly. Medfly existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilized in the field to catch Medflies which will reveal their presence, and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. In this paper, a machine learning system that can detect Medfly images on a picture and count their numbers is developed. A special trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilized. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the sticky band of the trap, they continue to spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are all buckled. The challenge here is that the machine learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilize pictures that contain trapped Medfly images that possess distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94% that is achieved by the machine learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favorable when compared to the success rates provided in the literature.

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