A novel automated image analysis method for counting the population of whiteflies on leaves of crops

Ghods, Sara; Shojaeddini, Vahhab

doi:10.18869/modares.jcp.5.1.59

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Applying texture and shape analysis, Ghods and Shojaeddini [21] created an algorithm that could identify whiteflies in plant leaves with 85% accuracy. Blasco et al [22] developed a prototype to monitor insect traps placed in the field by capturing and sending images of the trapped insects to…”

Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

“…Using threshold algorithms, Bodhe and Mukherji [21] developed a system to detect and count whiteflies using image analysis. Applying texture and shape analysis, Ghods and Shojaeddini [22] created an algorithm that could identify whiteflies in plant leaves with 85% accuracy. Blasco et al [23] developed a prototype to monitor insect traps placed in the field by capturing and sending images of the trapped insects to a remote server.…”

Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

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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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Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

Automatic Detection and Monitoring of Insect Pests—A Review

et al. 2020

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“…Miniaturized image sensors have reduced costs in fruit fly monitoring systems [22,23]. Counting algorithms [24] employ threshold algorithms [25], and texture and shape analysis [26] identified whiteflies in plant leaves with 85% accuracy. Recently, artificial intelligence has been effectively introduced in precise farming [27,28].…”

Section: Introductionmentioning

confidence: 99%

Mango Fruit Fly Trap Detection Using Different Wireless Communications

Hahn,

Valle,

Rendón

et al. 2023

Agronomy

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Fruit flies cause production losses in mango orchards affecting fruit quality. A National Campaign against Fruit Flies (NCFF) evaluates farm status using the fruit flies per trap per day index (FTD). Traps with attractant are installed manually within orchards in Mexico, but counting the flies trapped every week requires excessive numbers of trained personal. Electronic traps (e-traps) use sensors to monitor fruit fly population, saving labor and obtaining the real-time orchard infestation. The objective of this work was to acquire an image within a e-trap at 17:00 when an insect was detected and binarize the information in real-time to count the number of flies. Each e-trap was implemented with a polyethylene PET bottle screwed to a tap containing an ESP32-CAM camera. E-traps from several hectares of mango trees were sampled and transmitted through WSN wireless sensor networks. This original system presents a star topology network within each hectare with the long range LoRa transceiver at the central tower. It receives the fly count from five e-traps and finally transmits data to the house tower end point. Another contribution of this research was the use of a DJI mini2 for acquiring the e-trap data, and the 8-ha flight took 15 min and 35 s. This period can be reduced if the drone flies higher.

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“…Previous whitefly detection studies have been mainly based on image processing to detect objects in an image and then use machine learning algorithms to classify them. A method for counting whiteflies on the leaves of crops was proposed by [1]. In the study, a hypothesis framework was defined to distinguish pests from other particles in an image; then, color, texture and shape analysis was performed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application

et al. 2022

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In this study, we have compared YOLOv4, a single-shot detector to Faster-RCNN, a two-shot detector to detect and classify whiteflies on yellow-sticky tape (YST). An IoT remote whitefly monitoring station was developed and placed in a whitefly rearing room. Images of whiteflies attracted to the trap were recorded 2× per day. A total of 120 whitefly images were labeled using labeling software and split into a training and testing dataset, and 18 additional yellow-stick tape images were labeled with false positives to increase the model accuracy from remote whitefly monitors in the field that created false positives due to water beads and reflective light on the tape after rain. The two-shot detection model has two stages: region proposal and then classification of those regions and refinement of the location prediction. Single-shot detection skips the region proposal stage and yields final localization and content prediction at once. Because of this difference, YOLOv4 is faster but less accurate than Faster-RCNN. From the results of our study, it is clear that Faster-RCNN (precision—95.08%, F-1 Score—0.96, recall—98.69%) achieved a higher level of performance than YOLOv4 (precision—71.77%, F-1 score—0.83, recall—73.31%), and will be adopted for further development of the monitoring station.

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A novel automated image analysis method for counting the population of whiteflies on leaves of crops

Cited by 6 publications

References 11 publications

Automatic Detection and Monitoring of Insect Pests—A Review

Automatic Detection and Monitoring of Insect Pests—A Review

Mango Fruit Fly Trap Detection Using Different Wireless Communications

Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application

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