Ruy de Oliveira
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ruy de Oliveira has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Guilherme Pires Silva de Almeida
Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso (IFMT), Cuiabá 78005-200, Brazil
1 shared publication - Heyde Francielle Do Carmo França
Goiano Federal Institute, Rio Verde 75901-970, GO, Brazil
1 shared publication - Leandro Rodrigues da Silva Souza
Postgraduate Program in Biomedical Engineering, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
1 shared publication - Leonardo Nazário Silva dos Santos
Laboratório de Hidráulica e Irrigação, Instituto Federal de Educação, Ciência e Tecnologia Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, Rio Verde 75901-970, GO, Brazil
1 shared publication - Marconi Batista Teixeira
Laboratório de Hidráulica e Irrigação, Instituto Federal de Educação, Ciência e Tecnologia Goiano, Campus Rio Verde, Rodovia Sul Goiana, Km 01, Rio Verde 75901-970, GO, Brazil
1 shared publication - Mario De Oliveira
Birmingham City University
1 shared publication
Latest Publications
- Open Access
Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset
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%.
Open AccessPerformance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset
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%.