Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring

Sun, Yu; Liu, Xuanxin; Yuan, Mingshuai; Ren, Lili; Wang, Jianxin; Chen, Zhibo

doi:10.1016/j.biosystemseng.2018.10.012

Cited by 72 publications

(54 citation statements)

References 13 publications

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“…NCuts with the optical flow angle as weight function achieved the most accurate results. K-means clustering was one of the classifiers tested in [45], and it has also been used to enhance the results produced by deep learning models [50].…”

Section: Pest Detection Methodsmentioning

confidence: 99%

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Barbedo

2020

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Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.

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Section: Pest Detection Methodsmentioning

confidence: 99%

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Barbedo

2020

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“…The proposed RTBnet was a consequence of precise fitting and improvement to best in class onestage profound learning identifier RetinaNet. The key understanding behind the RTBnet configuration was to fit the identifier to the particular in-trap recognizable proof situation [13]. Yao et al (2017) has built up a three-layer discovery strategy is plausible and compelling for the recognizable proof of various formative phases of planthoppers on rice plants in paddy fields [14].…”

Section: Existing Methodsmentioning

confidence: 99%

Identification of Pests on Plants Using Clustering and Hybrid Approaches

Karkuzhali¹

2019

IJEAT

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Enormous agricultural yield is lost each year, because of quick pervasion by pest and insects. A great deal of research is being done worldwide to recognize logical procedures for early discovery/identification of these bio-aggressors. In the past years, a few methodologies dependent on computerization and digital image processing have become known to address this issue. The greater part of the calculations focus on pest identification and location, restricted to a greenhouse environment. Likewise, they include a few complex computations to accomplish the equivalent. In this paper, we developed a unique algorithmic approach to isolate and distinguish pest utilizing clustering and hybrid approaches. The proposed method includes decreased computational complexity and pest detection in green house environment. The whitefly, a bio-aggressor which represents a risk to a huge number of harvests, was picked as the pest of enthusiasm for this paper. The calculation was tried for a few whiteflies influencing various leaves and an accuracy of 96% of whitefly recognition was accomplished.

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“…However, solutions for sensor-based monitoring of insects and other invertebrates in their natural environment are emerging (34). The innovation and development is primarily driven by agricultural research to predict occurrence and abundance of beneficial and pest insect species of economic importance (35)(36)(37), to provide more efficient screening of natural products for invasive insect species (38), or to monitor disease vectors such as mosquitos (39,40). The most commonly used sensors are cameras, radar, and microphones.…”

Section: Sensor-based Insect Monitoringmentioning

confidence: 99%

“…The images are collected by a microcomputer and transmitted to a remote server where they are analysed. Other solutions have embedded a digital camera and a microprocessor that can count trapped individuals in real-time using object-detection based on an optimized deep learning model (37). In both these cases, deep learning networks are trained to recognize and count the number of single pest species.…”

Section: Image-based Solutions For In Situ Monitoringmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2020

Preprint

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ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statementInsect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.

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Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring

Cited by 72 publications

References 13 publications

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

Identification of Pests on Plants Using Clustering and Hybrid Approaches

Deep learning and computer vision will transform entomology

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