AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot

• URL: http://arxiv.org/abs/2608.29237v1
• Date: Sat, 29 Aug 2026 12:49:54 GMT
• Title: AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot
• Abstract summary: 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.
• Score: 3.4609543125371354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Monitoring (AGRICAM), a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems. AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows, without disrupting farm operations or insect behaviour. The platform integrates two RGB cameras, microclimate sensors, GPS and RFID modules, motion sensors, and 4G cellular network connectivity for data transmission. A web interface enables remote device configuration and scheduling. The system autonomously captures video and image data of insects' locations and local environmental conditions. These are transferred to the cloud and analysed using computer vision models to quantify pollinator visitation and spatio-temporal activity variation. We deployed the system on a commercial blueberry farm to demonstrate and test its capability. It successfully mapped insect pollination patterns across 80 m long industrial polytunnels over 30 hours. This data enabled spatial analyses of insect activity we used to confirm a uniform pollinator distribution within polytunnels, as desired by the farm management team. The data also highlighted variation of insect activity associated with time of day and microclimate. AGRICAM therefore has been shown to be a scalable, automated crop pollination monitor that can support data-driven decisions to enhance pollination management, thereby improving crop productivity and food security.

Related papers

• SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring [69.91458772455071]
  emphSkyDrive is a framework that utilizes drone-based traffic monitoring to provide efficient supervision for autonomous driving agents in a new environment.<n>Our findings show that aerial traffic monitoring is an efficient and scalable data source for adapting autonomous driving systems in new cities.
  arXiv  Detail & Related papers  (2026-08-25T20:47:09Z)
• 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)
• An IoT-Based Smart Plant Monitoring and Irrigation System with Real-Time Environmental Sensing, Automated Alerts, and Cloud Analytics [0.0]
  This paper presents a comprehensive IoT-based smart plant monitoring system.<n>It integrates multiple environmental sensors with automated irrigation and cloud analytics.<n>With a total implementation cost of $45.20, this system provides an affordable, scalable solution for precision agriculture and smart farming.
  arXiv  Detail & Related papers  (2026-01-22T10:33:31Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• Wandering around: A bioinspired approach to visual attention through object motion sensitivity [40.966228784674115]
  Active vision enables dynamic visual perception, offering an alternative to static feedforward architectures in computer vision.<n>Event-based cameras, inspired by the mammalian retina, enhance this capability by capturing asynchronous scene changes.<n>To distinguish moving objects while the event-based camera is in motion the agent requires an object motion segmentation mechanism.<n>This work presents a Convolutional Neural Network bio-inspired attention system for selective attention through object motion sensitivity.
  arXiv  Detail & Related papers  (2025-02-10T18:16:30Z)
• RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing [2.1038216828914145]
  Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying essential nutrients within plants as well as detecting diseases and water stress.<n>This work introduces RoMu4o, a robotic manipulation unit for orchard operations offering an automated solution for proximal hyperspectral leaf sensing.
  arXiv  Detail & Related papers  (2025-01-18T01:04:02Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation [42.39035033967183]
  Service robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Existing methods, however, often fall short in generalizing to new crops and environmental conditions. We propose a novel approach to enhance domain generalization using knowledge distillation.
  arXiv  Detail & Related papers  (2023-04-03T14:28:29Z)
• Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination [6.2997667081978825]
  Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Current computer vision facilitated insect tracking in complex outdoor environments is restricted in spatial coverage. This article introduces a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction.
  arXiv  Detail & Related papers  (2022-05-10T05:11:28Z)
• 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)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

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.