&amp;lt;i&amp;gt;An Online Unsupervised Deep Learning Approach for an Automated Pest Insect Monitoring System&amp;lt;/i&amp;gt;

Rustia, Dan Jeric Arcega; Chao, Jun–Jee; Chung, Jui‐Yung; Lin, Ta‐Te

doi:10.13031/aim.201900477

Cited by 14 publications

(13 citation statements)

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“…Finally, contextual regions of interest were used to improve detection accuracy. A two-step deep learning approach for classifying and counting five pest types in images of traps was adopted in [33]. The detection step, in which locations of the insects on the sticky paper are determined, was performed by the Tiny YOLO v3 object detector.…”

Section: Pest Classification Methodsmentioning

confidence: 99%

“…Two other types of deep learning strategies have been investigated for pest classification. Besides being used in [33] (see above), YOLO has been investigated in [65]. This work employed a two-step machine learning approach for the classification of six pest species in images of traps.…”

Section: Pest Classification Methodsmentioning

confidence: 99%

“…These are relatively general terms that yielded a large number of irrelevant results, but it was important to keep the search broad in order to avoid missing relevant matches. Only investigations published in peer-reviewed journals were selected, with the exception of [33], which is a conference article reporting some relevant findings. A second search considering the reference lists of the articles selected in the first step was also conducted.…”

Section: State Of the Art Of Pest Monitoring Using Digital Images Andmentioning

confidence: 99%

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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 Classification Methodsmentioning

confidence: 99%

Section: Pest Classification Methodsmentioning

confidence: 99%

Section: State Of the Art Of Pest Monitoring Using Digital Images Andmentioning

confidence: 99%

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

Barbedo

2020

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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%

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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“…The collected data was sent to the server present at the Pest Management System (PMS), which will help to PMS for timely control of the pest population on the crop field. Rustia et al [ 12 ] use the IoT network and wireless imaging system to develop the remote greenhouse pest monitoring system. The imaging system uses k-means color clustering and blob counting algorithm to automatically count the insect on the trap sheet.…”

Section: Introductionmentioning

confidence: 99%

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam

Mohan

Pookkuttath

et al. 2020

Sensors

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Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

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<i>An Online Unsupervised Deep Learning Approach for an Automated Pest Insect Monitoring System</i>

Cited by 14 publications

References 0 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

Deep learning and computer vision will transform entomology

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

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