Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset
de Almeida, Guilherme Pires Silva and dos Santos, Leonardo Nazário Silva and da Silva Souza, Leandro Rodrigues and da Costa Gontijo, Pablo and de Oliveira, Ruy and Teixeira, Matheus Cândido and de Oliveira, Mario A. and Teixeira, Marconi Batista and do Carmo França, Heyde Francielle (2024) Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset. Agronomy, 14 (10). p. 2194. ISSN 2073-4395
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Abstract
One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model relies on computer vision techniques, including You Only Look Once (YOLO) and Detectron2, and adapts them to lightweight formats—TensorFlow Lite (TFLite) and Open Neural Network Exchange (ONNX)—for resource-constrained devices. Our method leverages two datasets: a comprehensive one and a smaller sample for comparison purposes. With this setup, the authors aimed at using these two datasets to evaluate the performance of the computer vision models and subsequently convert the best-performing models into TFLite and ONNX formats, facilitating their deployment on edge devices. The results are promising. Even in the worst-case scenario, where the ONNX model with the reduced dataset was compared to the YOLOv9-gelan model with the full dataset, the precision reached 87.3%, and the accuracy achieved was 95.0%.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/agronomy14102194 | ||||||
| Dates: |
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| Uncontrolled Keywords: | Spodoptera frugiperda, Diceraeus ssp., Dalbulus maidis, Diabrotica speciosa, deep learning, computer vision, pest control, agronomy, grain production, ONNX, TFLite | ||||||
| Subjects: | CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > College of Engineering | ||||||
| Depositing User: | Gemma Tonks | ||||||
| Date Deposited: | 26 Sep 2024 13:27 | ||||||
| Last Modified: | 26 Sep 2024 13:27 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/15875 |
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