Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

Silva, Diego F.; Souza, Valdir de; Batista, Gustavo E. A. P. A.; Keogh, Eamonn; Ellis, Daniel P. W.

doi:10.1109/icmla.2013.24

Cited by 42 publications

(24 citation statements)

References 15 publications

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“…Readers may already be familiar with applications of ML-and AI-based commercial technologies, e.g., music identification via real-time signal processing on commodity smartphone hardware; cameras having automatic facial recognition; and recommendation systems for consumers that inform users about movies, news stories, or products [11,12]. Further, AI technologies are used to monitor agricultural fields for insect types and populations, to manage power usage in computer server centers exceeding human performance, and are now being deployed in driverassisted and driverless vehicles [13][14][15][16].…”

Section: Selected Context From Outside Of Mpsementioning

confidence: 99%

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dimiduk

Holm

Niezgoda

2018

Integr Mater Manuf Innov

253

126

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The fields of machining learning and artificial intelligence are rapidly expanding, impacting nearly every technological aspect of society. Many thousands of published manuscripts report advances over the last 5 years or less. Yet materials and structures engineering practitioners are slow to engage with these advancements. Perhaps the recent advances that are driving other technical fields are not sufficiently distinguished from long-known informatics methods for materials, thereby masking their likely impact to the materials, processes, and structures engineering (MPSE). Alternatively, the diverse nature and limited availability of relevant materials data pose obstacles to machine-learning implementation. The glimpse captured in this overview is intended to draw focus to selected distinguishing advances, and to show that there are opportunities for these new technologies to have transformational impacts on MPSE. Further, there are opportunities for the MPSE fields to contribute understanding to the emerging machine-learning tools from a physics basis. We suggest that there is an immediate need to expand the use of these new tools throughout MPSE, and to begin the transformation of engineering education that is necessary for ongoing adoption of the methods.

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Section: Selected Context From Outside Of Mpsementioning

confidence: 99%

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dimiduk

Holm

Niezgoda

2018

Integr Mater Manuf Innov

253

126

View full text Add to dashboard Cite

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“…More importantly, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito. This paper is an extended revision of [32,33]. We provide a broader experimental evaluation that includes the classification based on similarity and the use of feature subset selection to analyze which feature extraction techniques provide the most relevant features for classification.…”

Section: Introductionmentioning

confidence: 99%

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Silva

Souza

Ellis

et al. 2015

J Intell Robot Syst

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Insects have a close relationship with the humanity, in both positive and negative ways. Mosquito borne diseases kill millions of people and insect pests consume and destroy around US $40 billion worth of food each year. In contrast, insects pollinate at least two-thirds of all the food consumed in the world. In order to control populations of disease vectors and agricultural pests, researchers in entomology have developed numerous methods including chemical, biological and mechanical approaches. However, without the knowledge of the exact location of the insects, the use of these techniques becomes costly and inefficient. We are developing a novel sensor as a tool to control disease vectors and agricultural pests. This sensor, which is built from inexpensive commodity electronics, captures insect flight information using laser light and classifies the insects according to their species. The use of machine learning techniques allows the sensor to automatically identify the species without human intervention. Finally, the sensor can provide real-time estimates of insect species with virtually no time gap between the insect identification and the delivery of population estimates. In this paper, we present our solution to the most important challenge to make this sensor practical: the creation of an accurate classification system. We show that, with the correct combination of feature extraction and machine learning techniques, we can achieve an accuracy of almost 90 % in the task of identifying the correct insect species among nine species. Specifically, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito.

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“…The classifying process can previously learn from wingbeat frequency data of different species of insects, and whenever a new insect approaches the trap, it will automatically classify and take the decision-release it or kill it. That was exactly what "De Souza and Silva proposed using machine learning techniques" [4,5].…”

Section: Introductionmentioning

confidence: 81%

“…The application of machine learning techniques to design intelligent traps, using a laser sensor, and audio analysis techniques have been used to help insect recognition [5]. The device developed by the authors is able to attract and distinguish harmful from beneficial insects.…”

Section: Mosquito's Trapmentioning

confidence: 99%

Use of Artificial Intelligence on the Control of Vector-Borne Diseases

Motta¹,

Badaró²,

Santos³

et al. 2019

Vectors and Vector-Borne Zoonotic Diseases

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Artificial intelligence has many fields of application with an increasing computational processing power, and the algorithms are reaching human performance on complex tasks. Entomological characterization of insects represents an essential activity to drive actions to control the vector-borne diseases. Identification of the species and sex of insects is essential to map and organize the control measurements by the public health system in most areas where transmission is actively occurring. In many places in the world, the methodology done for identification of the mosquitos is by visual examination from human trained researchers or technicians. This activity is time-consuming and requires several years of experience to have skills to do the job. This chapter addresses the application of artificial intelligence for identification of mosquitos associated with vector-borne diseases. Benefits, limitations, and challenges of the use of artificial intelligence on the control of vector-borne diseases are discussed in this review.

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Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

Cited by 42 publications

References 15 publications

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Use of Artificial Intelligence on the Control of Vector-Borne Diseases

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