Hardware and Software Support for Insect Pest Management

Suto, Jozsef

doi:10.3390/agriculture13091818

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Machine learning has long been applied in agriculture [1], and future technological developments in this industry heavily rely on automation. Applications of machine learning in agriculture include mapping [2,3], crop management [4][5][6], precision irrigation and spraying [7][8][9], livestock management [10][11][12], automated farm management [13][14][15], and pest control [16][17][18][19][20][21]. Machine learning applied to pest control is seen as a long-term solution to different problems in agriculture, such as non-destructive testing of the presence of insects inside the fruit [22,23].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

Banlawe,

dela Cruz

2023

Micromachines

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The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluated for best-fit use in the study. The rationale for selecting the acoustic sensors includes local availability and accessibility. Among the three sensors tested, the MEMS sensor had the best result. The data for acoustic frequency are acquired using the selected sensor, which is placed inside a soundproof chamber to minimize the noise and isolate the sound produced by each activity. The identified activity of the adult mango pulp weevil includes walking, resting, and mating. The Mel-frequency cepstral coefficient (MFCC) was used for feature extraction of the recorded audio and training of the SVM classifier. The study achieved 89.81% overall accuracy in characterizing mango pulp weevil activity.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

Banlawe,

dela Cruz

2023

Micromachines

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A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation

León-García,

Palomares,

Yousef-Yousef

et al. 2024

Applied Sciences

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The precise monitoring of insect pest populations is the foundation of Integrated Pest Management (IPM) for pests of plants, humans, and animals. Digital technologies can be employed to address one of the main challenges, such as reducing the IPM workload and enhancing decision-making accuracy. In this study, digital technologies are used to deploy an automated trap for capturing images of insects and generating centralized repositories on a server. Subsequently, advanced computational models can be applied to analyze the collected data. The study provides a detailed description of the prototype, designed with a particular focus on its remote reconfigurability to optimize repository quality; and the server, accessible via an API interface to enhance system interoperability and scalability. Quality metrics are presented through an experimental study conducted on the constructed demonstrator, emphasizing trap reliability, stability, performance, and energy consumption, along with an objective analysis of image quality using metrics such as RMS contrast, Image Entropy, Image sharpness metric, Natural Image Quality Evaluator (NIQE), and Modulation Transfer Function (MFT). This study contributes to the optimization of the current knowledge regarding automated insect pest monitoring techniques and offers advanced solutions for the current systems.

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Hardware and Software Support for Insect Pest Management

Abstract: In recent years, the achievements of machine learning (ML) have affected all areas of industry and it plays an increasingly important role in agriculture as well [...]

Cited by 2 publications

References 7 publications

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation

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