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Open AccessJournal ArticleDOI

Monitoring Pest Insect Traps by Means of Low-Power Image Sensor Technologies

TLDR
This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application.
Abstract
Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.).

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Citations
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Journal ArticleDOI

Insect pest monitoring with camera-equipped traps: strengths and limitations

TL;DR: The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps to support the use of software and image recognition algorithms to identify and/or count insect species from pictures.
Journal ArticleDOI

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

TL;DR: It is argued that smart traps communicating through IoT to report in real-time the level of the pest population from the field straight to a human controlled agency can, in the very near future, have a profound impact on the decision-making process in crop protection and will be disruptive of existing manual practices.
Journal ArticleDOI

Research on Recognition Model of Crop Diseases and Insect Pests Based on Deep Learning in Harsh Environments

TL;DR: In this paper, a convolution neural network is used to automatically identify crop diseases and insect pests, and the experimental results show that the overall recognition accuracy is 86.1% in this model, which verifies the effectiveness.
Journal ArticleDOI

Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

TL;DR: Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded.
Journal ArticleDOI

Review of agricultural IoT technology

TL;DR: In this paper , the authors systematically summarized the research status of agricultural IoT and analyzed the problems existing in agricultural IoT, and a forecast is given of the future development of the agricultural IoT.
References
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Journal ArticleDOI

Concepts and applications of trap cropping in pest management

TL;DR: A broader definition of trap cropping is proposed that encompasses the inherent characteristics of the trap crop plants themselves as well as the strategies associated with their deployment, which is more knowledge-intensive than many other forms of pest management.
Proceedings ArticleDOI

MeshEye: a hybrid-resolution smart camera mote for applications in distributed intelligent surveillance

TL;DR: MeshEye is introduced, an energy-efficient smart camera mote architecture that has been designed with intelligent surveillance as the target application in mind and basic vision algorithms for object detection, acquisition, and tracking are described and illustrated on real- world data.
Proceedings ArticleDOI

FireFly Mosaic: A Vision-Enabled Wireless Sensor Networking System

TL;DR: FireFly Mosaic is presented, a wireless sensor network image processing framework with operating system, networking and image processing primitives that assist in the development of distributed vision-sensing tasks and is the first wireless sensor networking system to integrate multiple coordinating cameras performing local processing.

CMUcam3: An Open Programmable Embedded Vision Sensor

TL;DR: TheCMUcam3 is the third generation of the CMUcam system and is designed to provide a flexible and easy to use open source development environment along with a more powerful hardware platform.
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