Artificial Intelligence, Machine Learning and IOT in Pest Management

Dwivedi, Mahavir; Chakravarthy, A. K.; George, Smitha; Raghavendra, K. V.

doi:10.1007/978-981-19-0264-2_4

Cited by 1 publication

(2 citation statements)

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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%

“…The MPW also responds to a blend of odors consisting of acetic acid (0.7%), decane (0.3%), acetone (4.4%), linalool (82.2%), ethyl benzoate (11.4%), and 2-methyl heptanone (1.0%), which caused a 70% rate of attraction from the MPW [16]. It is recommended that the best timing period is before the fruit reaches 4 cm, which occurs 3-4 weeks after fruiting [17,[35][36][37]. Ref.…”

Section: Introductionmentioning

confidence: 99%

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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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