Image-based Monitoring System Research Articles

Edge-based wireless imaging system for continuous monitoring of insect pests in a remote outdoor mango orchard

Mango production is a prominent tropical fruit industry worldwide. However, outdoor mango cultivation is susceptible to crop damage caused by insect pests and harsh environmental conditions. Integrated pest management (IPM) has emerged as a proposed solution to this problem. IPM utilizes data-driven and environmentally-friendly methods to suppress insect pest populations. Nevertheless, the collection of insect pest population data remains a laborious process, necessitating automation. This paper presents an image-based monitoring system to automatically record insect pest populations and environmental conditions in mango orchards. The system comprises solar-powered sensor nodes capable of periodically acquiring and analyzing sticky paper trap images. A modular deep learning-based algorithm was developed to detect and classify insect pests into seven classes, including major insect pests of mango such as thrips, mango leafhopper, and oriental fruit fly, with an average classification F1-score of 0.96. Unlike other insect counting algorithms, the algorithm reliably classifies insect pests according to different taxonomic levels even in non-laboratory environments. The monitoring system was tested and deployed in a remote mango orchard for over two years. The collected spatiotemporal information was analyzed to demonstrate the benefits of using the proposed system and recommend new IPM strategies. Temporal data analysis revealed a significant decrease in the count of selected insect pests after using the system, enabling identification of insect hotspots through statistical methods. This work presents a breakthrough in hardware and software solutions for developing smarter insect pest monitoring systems, leading to better IPM strategies.

Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer.

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.

Determination of static space occupied by individual weaner and growing pigs using an image-based monitoring system

AbstractIn the present study, precise, animal-based biometric data on the space needed for the body dimensions of individual pigs (static space) were collected. Per batch, two groups of eight piglets each were formed after weaning (35 days old). Using three-dimensional cameras that recorded a piglets’ pen from above and newly developed software, the static space of individuals was determined over 6 weeks. The area covered by an individual increased almost linearly with increasing body weight (R2 = 0.97). At the end of rearing (25 kg body weight), an individual covered 1704 cm2 in standing position, 1687 cm2 in sitting posture and 1798 cm2 in a recumbent position. According to the allometric equation: Space = k × body weight0.667, k values for the static space in standing position (k = 0.021), in recumbent position in general (k = 0.022) and in lateral recumbent posture (k = 0.027) were calculated. Compared with spatial requirements in different countries, the results of static space obtained in the present study revealed that pigs weighing 25 kg are provided with 0.09–0.18 m2 free space per pig which is not covered by the pig's body. This free space can be used as dynamic space needed for body movements or social interactions. The present study was not intended to enhance space recommendations in pig farming, but to demonstrate the amount of free space in a pigs’ pen. It was shown that innovative technologies based on image analysis offer completely new possibilities to assess spatial requirements for pigs.

Shape Sensing Monitoring System Based on Fiber-Optic Strain Measurements: Laboratory Tests

In the context of the characterization of the shape of flying sails whilst racing, a novel method for real-time form-finding of flexural beams based on strain measurements has been developed and implemented. The very large displacements and deformations occurred on the beam pose specific demands both to the algorithm for shape detection and to the materials and sensors to use in the practical implementation, which are described and addressed in this paper. The developed method is implemented by installation of fiber Bragg grating (FBG) sensors on a beam, which are used to evaluate the curvature at specific cross-sections. The deformation curve is reconstructed by integration of local curvatures and interpolation. The effectiveness of the developed method is verified by tests on a physical model. An image-based monitoring system is further employed to validate the concept and the tests conducted with the FBG sensors. This allows the performance of an additional series of sensitivity tests in order to define the most relevant measurement parameters and characterize the accuracy of the developed method.

Technical Note: Proposing a Low-Tech, Affordable, Accurate Stream Stage Monitoring System

Streamflow data are essential for water resources planning and decision making and are routinely analyzed to determine the impacts of climate change on hydrology. Unfortunately, current stream gauges, largely the responsibility of the U.S. Geological Survey (USGS) in the U.S. and similar agencies worldwide, are expensive to install and operate and are being steadily decommissioned. Part of the solution to this problem is a low-cost stream gauging system that is simple enough to use by people with little or no formal training in environmental monitoring. In this article, a low-cost, digital camera-based stream stage monitoring system is proposed, described, and tested. As a proof-of-concept, a time series was generated by taking digital pictures of a staff gauge at 3 h intervals over several weeks at a current USGS gauging site. The image-based stage heights closely matched the USGS gauge values, although significant stage height errors were evident in a small percentage (<3%) of the images. We identified the problem as being caused by shadows and irregular lighting and proposed a protocol for eliminating these errant images. When the obviously problematic images were removed, the relative differences between the image-based stages and USGS stages were approximately 5%. The next step is to develop an on-line system for post-processing the images so that watershed networks, citizen science organizations, K-12 educational institutions, and others can engage in stream monitoring and make their data freely available. We also propose some possible next steps for determining stream cross-section and flow velocity using this low-cost camera- or image-based monitoring system.

Development and Evaluation of a Seismic Monitoring System for Building Interiors—Part II: Image Data Analysis and Results

Previous experiences during earthquake events emphasize the need for new technologies for real-time monitoring and assessment of facilities with high-value nonstructural contents. Moreover, there is a substantial limitation in our ability to rapidly evaluate and identify potential hazard zones within a structure, exposing rescue workers, society, and the environment to unnecessary risks. A real-time image-based monitoring system, which is integrated with warning systems, would allow for improved channeling of resources and informed decision making for rescue workers and building owners. In recognition of these issues, in this paper, we describe a methodology for image-based tracking of seismically induced motions. The methodology includes the acquisition, calibration, and processing of image sequences to detect and track object features under seismic-event conditions. We address the issue of providing a reliable feature/object-detection and object-tracking methodology for an image sequence from a single camera view. In addition, we introduce an extension to the 2-D tracking approach by providing a 3-D feature tracking methodology when the camera array itself is affected by the seismic event. The methods presented are demonstrated using the data collected during the full-scale field vibration tests conducted on a vacant building that was damaged during the 1994 Northridge earthquake (presented in a companion paper). We present experimental tracking results of the implemented algorithms for a variety of objects and discuss additional challenges that emerge when image-based systems are used under these extreme conditions.