Generative Adversarial Network Based Image Augmentation for Insect Pest Classification Enhancement

Lu, Chen-Yi; Rustia, Dan Jeric Arcega; Lin, Ta‐Te

doi:10.1016/j.ifacol.2019.12.406

Cited by 53 publications

(31 citation statements)

References 5 publications

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“…A promising alternative is to use deep learning models for generating artificial images belonging to the class of interest. The most widely used approach to date is based on generative adversarial networks (106) and has shown promising performance in computer vision problems in general, as well as in ecological problems (107).…”

Section: Future Directionsmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

280

206

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Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

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Section: Future Directionsmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

280

206

View full text Add to dashboard Cite

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“…Furthermore, Lu et al [35] proposed a convolutional network (CNN) classifier model in combination with a generative adversarial network (GAN) image augmentation. For this method, a Raspberry Pi v2 camera was used, both whiteflies and thrips were analyzed, and synthetic images were created through the GAN-based data augmentation method, in order to enhance CNN classifier with limited image data.…”

Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

Automatic Detection and Monitoring of Insect Pests—A Review

et al. 2020

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Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

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“…Needing only a desired image output as a target to generate, this architecture can leverage smart training techniques to learn from a huge amount of data that are not extensively annotated. This is one of the main reasons why generative models have made large strides in our ability to successfully model complex, high-dimensional data in applications such as image generation [ 50 ], video generation [ 51 ] and point cloud completion [ 52 ] and why they have been implemented in many applications related to CD, including one-shot learning [ 53 ] and image interpolation [ 54 ].…”

Section: Representation Learning For Fine-grained Change Detectionmentioning

confidence: 99%

Representation Learning for Fine-Grained Change Detection

Mahony

Campbell

Krpálková

et al. 2021

Sensors

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Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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Generative Adversarial Network Based Image Augmentation for Insect Pest Classification Enhancement

Cited by 53 publications

References 5 publications

Deep learning and computer vision will transform entomology

Deep learning and computer vision will transform entomology

Automatic Detection and Monitoring of Insect Pests—A Review

Representation Learning for Fine-Grained Change Detection

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