A Proposed IoT Smart Trap using Computer Vision for Sustainable Pest Control in Coffee Culture

• URL: http://arxiv.org/abs/2004.04504v1
• Date: Thu, 9 Apr 2020 12:04:15 GMT
• Title: A Proposed IoT Smart Trap using Computer Vision for Sustainable Pest Control in Coffee Culture
• Abstract summary: The Internet of Things (IoT) is emerging as a multi-purpose technology with enormous potential for improving the quality of life in several areas. In this paper, a smart trap with IoT capabilities that uses computer vision to identify the insect of interest is proposed. The demonstration of proposed solution is exposed and the main conclusions are the perception about pest concentration at the plantation and the viability as alternative pest control over traditional control based on pesticides.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Internet of Things (IoT) is emerging as a multi-purpose technology with enormous potential for improving the quality of life in several areas. In particular, IoT has been applied in agriculture to make it more sustainable ecologically. For instance, electronic traps have the potential to perform pest control without any pesticide. In this paper, a smart trap with IoT capabilities that uses computer vision to identify the insect of interest is proposed. The solution includes 1) an embedded system with camera, GPS sensor and motor actuators; 2) an IoT middleware as database service provider, and 3) a Web application to present data by a configurable heat map. The demonstration of proposed solution is exposed and the main conclusions are the perception about pest concentration at the plantation and the viability as alternative pest control over traditional control based on pesticides.

Related papers

• AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot [3.4609543125371354]
  AGRICAM is a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems.<n>AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows.<n>System captures video and image data of insects' locations and local environmental conditions.
  arXiv  Detail & Related papers  (2026-08-29T12:49:54Z)
• Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms [0.28312565521926425]
  Plant diseases cause significant yield losses worldwide, with tomato crops particularly susceptible to early blight, late blight, and leaf mold.<n>To tackle this limitation, an artificial intelligence (AI) enabled cloud-edge architecture is proposed for autonomous crop monitoring.<n>This proposed architecture integrates Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), deep learning, Azure IoT Hub-based cloud analytics, and multi-platform interfaces.
  arXiv  Detail & Related papers  (2026-08-04T15:25:02Z)
• Context-Aware Pesticide Recommendation via Few-Shot Pest Recognition for Precision Agriculture [1.2744523252873352]
  This study presents a lightweight framework for pest detection and pesticide recommendation, designed for low-resource devices such as smartphones and drones.<n>The proposed framework includes two main components. The first is a Pest Detection Module that uses a compact, lightweight convolutional neural network (CNN) combined with meta-learning to accurately identify pests.<n>The second is a Pesticide Recommendation Module that incorporates environmental factors like crop type and growth stage to suggest safe and eco-friendly pesticide recommendations.
  arXiv  Detail & Related papers  (2026-01-01T07:33:09Z)
• Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
  This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge.<n>We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic.<n>This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
  arXiv  Detail & Related papers  (2025-04-22T09:40:05Z)
• IoT-LM: Large Multisensory Language Models for the Internet of Things [70.74131118309967]
  IoT ecosystem provides rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio. Machine learning presents a rich opportunity to automatically process IoT data at scale. We introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem.
  arXiv  Detail & Related papers  (2024-07-13T08:20:37Z)
• MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae) [0.0]
  This paper presents the design, development, and testing of an innovative system named MosquIoT. Based on traditional ovitraps with embedded Internet of Things (IoT) and Tiny Machine Learning (TinyML) technologies, it enables the detection and quantification of Ae. aegypti eggs.
  arXiv  Detail & Related papers  (2024-01-29T16:08:18Z)
• SpectroGLY: A Low-Cost IoT-Based Ecosystem for the Detection of Glyphosate Residues in Waters [0.0]
  Glyphosate contamination in waters is becoming a major health problem. This paper presents the design, development and testing of an innovative low-cost VIS-NIR (Visible and Near-Infrared) spectrometer (called SpectroGLY) Thanks to its portability, it can be used in any context and provides results in 10 minutes.
  arXiv  Detail & Related papers  (2024-01-29T09:56:35Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks [82.964958051535]
  Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society.
  arXiv  Detail & Related papers  (2023-10-27T02:58:11Z)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future [6.422895251217666]
  This paper reviews forensic and security issues associated with IoT in different fields. Most IoT devices are vulnerable to attacks due to a lack of standardized security measures. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system.
  arXiv  Detail & Related papers  (2023-09-06T04:41:48Z)
• IoT-based Route Recommendation for an Intelligent Waste Management System [61.04795047897888]
  This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account.
  arXiv  Detail & Related papers  (2022-01-01T12:36:22Z)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• Automated Pest Detection with DNN on the Edge for Precision Agriculture [0.0]
  This paper presents an embedded system enhanced with machine learning (ML) functionalities, ensuring continuous detection of pest infestation inside fruit orchards. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.
  arXiv  Detail & Related papers  (2021-08-01T10:17:48Z)
• sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction [19.525915957304527]
  This article presents the novel concept of Internet-of-Agro-Things (IoAT) with an example of automated plant disease prediction. It consists of solar enabled sensor nodes which help in continuous sensing and automating agriculture. The proposed system has adopted the use of an energy efficient way of powering using solar energy.
  arXiv  Detail & Related papers  (2020-05-09T05:54:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.