Insect Identification and Counting in Stored Grain: Image Processing Approach and Application Embedded in Smartphones

Zhu, Chunhua; Wang, Jiaojiao; Líu, Hao; Mi, Huan

doi:10.1155/2018/5491706

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…It is worth noting that several cell phone sound detection apps have been developed, e.g., [32], some of which already have been adapted for insect detection [33]. There is potential that these or similar apps can be modified or further developed for stored product insect detection by incorporation of the DAVIS [9] signal analysis algorithms [34]. Microphone or piezoelectric sensor systems can be coupled with the smart phones and the app creating a useful tool for scouting for insects in grain stores.…”

Section: Discussionmentioning

confidence: 99%

Acoustic, Pitfall Trap, and Visual Surveys of Stored Product Insect Pests in Kenyan Warehouses

Njoroge

Affognon

Richter

et al. 2019

Insects

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Grain production is an important component of food security in Kenya but due to environmental conditions that favor rapid growth of insect populations, farmers and other agricultural stakeholders face ongoing and novel challenges from crop and stored product pest insects. To assist development of methods to reduce economic losses from stored product insect pests in Kenya, acoustic, visual, and pitfall trap surveys were conducted in five grain storage warehouses. Two commercially available acoustic systems successfully detected the pests of greatest economic importance, Sitophilus zeamais (Motschulsky) and Prostephanus truncatus (Horn). Other insects of lesser economic importance also were observed in the visual surveys, including Sitotroga cerealella (Olivier) (Lepidoptera: Gelechiidae), and Tribolium castaneum (Herbst). This study demonstrated that the use of acoustic technology with visual surveys and pitfall traps can help managers to identify and target infestations within their warehouses, enabling them to reduce postharvest losses. With most warehouses being located in relatively noisy urban or peri-urban areas, background noise considerations are being incorporated into the design of future acoustic detectors for stored pest infestations. Kenya must import grain yearly to meet consumption needs; however, if the current yearly postharvest losses of 20–30% in warehouses decreased, import costs could be reduced considerably.

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Section: Discussionmentioning

confidence: 99%

Acoustic, Pitfall Trap, and Visual Surveys of Stored Product Insect Pests in Kenyan Warehouses

Njoroge

Affognon

Richter

et al. 2019

Insects

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show abstract

“…After the agronomist has completed their review, the system will send an email to the farmers informing them of the quality of their current paddy plot. The researchers from [5] suggested an image processing technique and a smartphone application to recognize and count insects. The nonuniform brightness of insect images obtained with mobile phones is released using a sliding window-based binarization, and then connected domain-based histogram statistics are utilized to identify and count the insects in stored grain.…”

Section: Background Studymentioning

confidence: 99%

An Efficient System for Real-time Mobile Smart Device-based Insect Detection

Doan¹

2022

IJACSA

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In recent years, the rapid development of many pests and diseases has caused heavy damage to the agricultural production of many countries. However, it is difficult for farmers to accurately identify each type of insect pest, and yet they have used a large number of pesticides indiscriminately, causing serious environmental pollution. Meanwhile, spraying pesticides is very expensive, and thus developing a system to identify cropdamaging pests early will help farmers save a lot of money while also contributing to the development of sustainable agriculture. This paper presents a new efficient deep learning system for realtime insect image recognition on mobile devices. Our system achieved an accuracy of mAP@0.5 with the YOLOv5-S model of 70.5% on the 10 insect dataset and 42.9% on the IP102 largescale insect dataset. In addition, our system can provide more information to farmers about insects such as biological characteristics, distribution, morphology, and pest control measures. From there, farmers can take appropriate measures to prevent pests and diseases, helping reduce production costs and protecting the environment.

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“…The marker watershed was used to further segment adherent cells to avoid over-segmentation and improve counting accuracy. Finally, cell counting was realized by detecting the connected domains in binary images (Zhu, Wang, Liu, & Mi, 2018), which dramatically reduces the subjective influence of laboratory personnel. The proposed method for cell counting is fast, has high accuracy and efficiency, robust reliability, and low cost, and is of great significance to the fundamental researches of biomedicine.…”

Section: Related Workmentioning

confidence: 99%

Automatic cell counting for phase‐contrast microscopic images based on a combination of Otsu and watershed segmentation method

Lin

Diao

et al. 2021

Microscopy Res & Technique

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Cell counting plays a vital role in biomedical researches. However, manual cell counting is time-consuming, laborious, and low efficiency and has a high counting error rate problem. An automatic counting approach for Hela cells of phase-contrast microscopic images is proposed based on the combination of Otsu and watershed segmentation methods to solve the mentioned issues. Firstly, image preprocessing is performed. Secondly, the Otsu method was used to obtain an automatic global optimal threshold for segmentation to achieve batch counting of images. Thirdly, the marker watershed was performed to separate adherent cells and to avoid oversegmentation simultaneously. Finally, cells in phase-contrast microscopic images were counted by detecting the numbers of connected domains in the binary image.

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Insect Identification and Counting in Stored Grain: Image Processing Approach and Application Embedded in Smartphones

Cited by 15 publications

References 10 publications

Acoustic, Pitfall Trap, and Visual Surveys of Stored Product Insect Pests in Kenyan Warehouses

Acoustic, Pitfall Trap, and Visual Surveys of Stored Product Insect Pests in Kenyan Warehouses

An Efficient System for Real-time Mobile Smart Device-based Insect Detection

Automatic cell counting for phase‐contrast microscopic images based on a combination of Otsu and watershed segmentation method

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