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Details

  • Name

    Ana Cláudia Teixeira
  • Role

    Research Assistant
  • Since

    09th March 2022
Publications

2023

A Systematic Review on Automatic Insect Detection Using Deep Learning

Authors
Teixeira, AC; Ribeiro, J; Morais, R; Sousa, JJ; Cunha, A;

Publication
AGRICULTURE-BASEL

Abstract
Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches-standard and adaptable-for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management.

2023

Automatic Identification of Public Lighting Failures in Satellite Images: A Case Study in Seville, Spain

Authors
Teixeira, AC; Batista, L; Carneiro, G; Cunha, A; Sousa, J;

Publication
International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract
Public lighting is crucial for maintaining the safety and well-being of communities. Current inspection methods involve examining the luminaires during the day, but this approach has drawbacks, including energy consumption, delay in detecting issues, and high costs and time investment. Utilising deep learning based automatic detection is an advanced method that can be used for identifying and locating issues in this field. This study aims to use deep learning to automatically detect burnt-out street lights, using Seville (Spain) as a case study. The study uses high-resolution night time imagery from the JL1-3B satellite to create a dataset called NLight, which is then divided into three subsets: NL1, NL2, and NT. The NL1 and NL2 datasets are used to train and evaluate YOLOv5 and YOLOv7 segmentation models for instance segmentation of streets. And then, distance outliers were detected to find the lights off. Finally, the NT dataset is used to evaluate the effectiveness of the proposed methodology. The study finds that YOLOv5 achieved a mask mAP of 57.7%, and the proposed methodology had a precision of 30.8% and a recall of 28.3%. The main goal of this work is accomplished, but there is still space for future work to improve the methodology. © 2023 IEEE.

2023

Street Light Segmentation in Satellite Images Using Deep Learning

Authors
Teixeira, AC; Carneiro, GA; Filipe, V; Cunha, A; Sousa, JJ;

Publication
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, CA, USA, July 16-21, 2023

Abstract
Public lighting plays a very important role for society's safety and quality of life. The identification of faults in public lighting is essential for the maintenance and prevention of safety. Traditionally, this task depends on human action, through checking during the day, representing expenditure and waste of energy. Automatic detection with deep learning is an innovative solution that can be explored for locating and identifying of this kind of problem. In this study, we present a first approach, composed of several steps, intending to obtain the segmentation of public lighting, using Seville (Spain) as case study. A dataset called NLight was created from a nighttime image taken by the JL1-3B satellite, and four U-Net and FPN architectures were trained with different backbones to segment part of the NLight. The U-Net with InceptionResNetv2 proved to be the model with the best performance, obtained 761 of 815, correct locations (93.4%). This model was used to predict the segmentation of the remaining dataset. This study provides the location of lamps so that we can identify patterns and possible lighting failures in the future. © 2023 IEEE.

2023

Evaluating YOLO Models for Grape Moth Detection in Insect Traps

Authors
Teixeira, AC; Carneiro, G; Morais, R; Sousa, J; Cunha, A;

Publication
International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract
The grape moth is a common pest that affects grapevines by consuming both fruit and foliage, rendering grapes deformed and unsellable. Integrated pest management for the grape moth heavily relies on pheromone traps, which serve a crucial function by identifying and tracking adult moth populations. This information is then used to determine the most appropriate time and method for implementing other control techniques. This study aims to find the best method for detecting small insects. We evaluate the following recent YOLO models: v5, v6, v7, and v8 for detecting and counting grape moths in insect traps. The best performance was achieved by YOLOv8, with an average precision of 92.4% and a counting error of 8.1%. © 2023 IEEE.

2023

Transfer-Learning On Land Use And Land Cover Classification

Authors
Carneiro, G; Teixeira, A; Cunha, A; Sousa, J;

Publication
International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract
In this study, we evaluated the use of small pre-trained 3D Convolutional Neural Networks (CNN) on land use and land cover (LULC) slide-window-based classification. We pretrained the small models in a dataset with origin in the Eurosat dataset and evaluated the benefits of the transfer-learning plus fine-tuning for four different regions using Sentinel-2 L1C imagery (bands of 10 and 20m of spatial resolution), comparing the results to pre-trained models and trained from scratch. The models achieved an F1 Score of between 0.69-0.80 without significative change when pre-training the model. However, for small datasets, pre-training the model improved the classification by up to 3%. © 2023 IEEE.