Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam, Balakrishnan; Mohan, Rajesh Elara; Pookkuttath, Sathian; Gómez, Braulio Félix; Borusu, Charan Satya Chandra Sairam; Teng, Tey Wee; Tamilselvam, Yokhesh Krishnasamy

doi:10.3390/s20185280

Cited by 62 publications

(37 citation statements)

References 45 publications

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“…Furthermore, the algorithm’s detection efficiency was estimated using standard performance measures (statistical measures), such as accuracy, precision, recall, and

[ 28 , 29 ]. Table 2 shows the statistical measures for the offline experimental results.…”

Section: Resultsmentioning

confidence: 99%

“…The Faster RCNN object detection framework was used in the rodent activity detection task, an optimal algorithm for detecting small objects from a complex background. Figure 6 shows the architecture of the Faster RCNN algorithm [ 28 ]. It comprises a ResNet 101 (Residual Neural Network) feature extractor, a Region Proposal Network (RPN), a detector, and a classifier head.…”

Section: Overview Of Proposed Systemmentioning

confidence: 99%

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AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Ramalingam

Tun²,

Mohan

et al. 2021

Sensors

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Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon." The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.

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“…Furthermore, the algorithm’s detection efficiency was estimated using standard performance measures (statistical measures), such as accuracy, precision, recall, and

[ 28 , 29 ]. Table 2 shows the statistical measures for the offline experimental results.…”

Section: Resultsmentioning

confidence: 99%

Section: Overview Of Proposed Systemmentioning

confidence: 99%

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Ramalingam

Tun²,

Mohan

et al. 2021

Sensors

Self Cite

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“…The semantic segmentation is carried out using ResNet50 [ 41 ] and Pyramid Pooling Module (PPM) encoder-decoder method trained on ADE20K indoor dataset [ 42 , 43 , 44 , 45 ]. The mask corresponding to the class “floor“ has been extracted from the semantic segmentation output.…”

Section: Exploration Strategymentioning

confidence: 99%

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Pathmakumar

Kalimuthu

Elara

et al. 2021

Sensors

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Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.

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“…"Ramalingam, B., Mohan, R.E., Pookkuttath, S., Gómez, B.F., Sairam Borusu, C.S.C., Wee Teng, T. and Tamilselvam, Y.K in 2020" [10] researched that early insect identification and control (human physical conditions as an example houses, hospitals hotels, parks, camps, flooring, industries related to food etc) and agricultural farms were important for developed environments. These pest control steps are currently labor-dependent manual, repetitive, unpredictable and time-consuming activities.…”

Section: Remote Insects Trap Monitoring System Using Deep Learning Framework and Iotmentioning

confidence: 99%

Recognition and Early Stage Detection of Phytophthora in a Crop Farm Using IoT

Vajpayee¹,

Yogi²

2021

Agro-Economic Risks of Phytophthora and an Effective Biocontrol Approach

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Detection of agricultural plant pests is seen as one of the farmers’ problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers’ income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection.

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Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Cited by 62 publications

References 45 publications

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Recognition and Early Stage Detection of Phytophthora in a Crop Farm Using IoT

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