Inspection of insect sticky paper traps is an essential task for an effective integrated pest management (IPM) programme. However, identification and counting of the insect pests stuck on the traps ...is a very cumbersome task. Therefore, an efficient approach is needed to alleviate the problem and to provide timely information on insect pests. In this research, an automatic method for the multi‐class recognition of small‐size greenhouse insect pests on sticky paper trap images acquired by wireless imaging devices is proposed. The developed algorithm features a cascaded approach that uses a convolutional neural network (CNN) object detector and CNN image classifiers, separately. The object detector was trained for detecting objects in an image, and a CNN classifier was applied to further filter out non‐insect objects from the detected objects in the first stage. The obtained insect objects were then further classified into flies (Diptera: Drosophilidae), gnats (Diptera: Sciaridae), thrips (Thysanoptera: Thripidae) and whiteflies (Hemiptera: Aleyrodidae), using a multi‐class CNN classifier in the second stage. Advantages of this approach include flexibility in adding more classes to the multi‐class insect classifier and sample control strategies to improve classification performance. The algorithm was developed and tested for images taken by multiple wireless imaging devices installed in several greenhouses under natural and variable lighting environments. Based on the testing results from long‐term experiments in greenhouses, it was found that the algorithm could achieve average F1‐scores of 0.92 and 0.90 and mean counting accuracies of 0.91 and 0.90, as tested on a separate 6‐month image data set and on an image data set from a different greenhouse, respectively. The proposed method in this research resolves important problems for the automated recognition of insect pests and provides instantaneous information of insect pest occurrences in greenhouses, which offers vast potential for developing more efficient IPM strategies in agriculture.
Display omitted
•The system can monitor insect count and environmental parameters simultaneously.•The average temporal accuracy of the insect pest counting algorithm is 93%.•Spatial and temporal ...information of insect activity can be effectively obtained.•Insect activity affected by environment can be investigated with the system.
This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. 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. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).
The unavailability and variability of training samples are the two essential concerns in the training of deep neural network models for image classification. For automated image monitoring systems, ...these problems are difficult when training a model through supervised learning methods because of the time and effort required. This paper proposes an adaptive solution to this problem by applying online semi-supervised learning to an automated insect pest monitoring system. The method used includes unsupervised pseudo-labelling of insect images and the training of semi-supervised classifier models for insect image recognition. The pseudo-labelling algorithm includes three major components: image labelling, label reconfirmation, and sample cleaning. Experiments were conducted on two unlabelled 1-year insect image datasets to evaluate the efficacy of the proposed method. It was found that the pseudo-labelling algorithm could achieve accuracy up to 0.963, hence enabling automated training data collection. The temporal improvement of the insect recognition performance by including new training data to retrain the classifier model was comparable in performance to the supervised learning approach as evaluated by cluster density, silhouette score, and F1-score. The proposed method was also able to automatically collect quality samples and train models regardless of the complexity of the images, making it a good alternative to replace laborious supervised learning. The proposed method can prevent contamination of a training dataset when images from new locations are collected. The presented techniques may also be used in other continuous learning applications that require automated training data collection and online model update.
Display omitted
•Semi-supervised learning method for an insect pest monitoring system was proposed.•Accuracy of the pseudo-labelling algorithm can be as high as 0.963.•Characteristics and variation of training data in classifier models were assessed.•Performance of classifier models can be improved through semi-supervised learning.•Techniques can be used for automated training data collection and model update.
Display omitted
•An image-based system for monitoring pests in mango orchards was developed.•System overcame bottlenecks of pest monitoring in remote and outdoor environment.•Deep learning algorithm ...detects and recognizes mango pests with an F1-score of 0.96.•More than 2 years of spatiotemporal data reflected trends in pest behavior.•Spatiotemporal data was used to realize new data-driven IPM strategies.
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.
This study combines image processing and enzyme to conduct the research and develope a detection system for adhesive cells detachment. First, a PCI video capture card is used to capture the ...microscope images into the computer, a program is edited to detect cell morphology and determine the detachment status of the cells, and then cell morphology was compared by detection data (roundness and centroid locating), in order to establish rules to detect the cell suspensions. This experiment will find the boundary sensitivity parameter, "fudgeFactor", and the rules for the detection of cell suspension. The multiple displacement difference compliance rate (90%) is greater than displacement difference compliance rate (70%), and compared with the roundness detection, then cell detachment can be accurately measured.
Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are life-threatening adverse drug reactions characterized by massive epidermal necrosis, in which the specific danger signals ...involved remain unclear. Here we show that blister cells from skin lesions of SJS-TEN primarily consist of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, and both blister fluids and cells were cytotoxic. Gene expression profiling identified granulysin as the most highly expressed cytotoxic molecule, confirmed by quantitative PCR and immunohistochemistry. Granulysin concentrations in the blister fluids were two to four orders of magnitude higher than perforin, granzyme B or soluble Fas ligand concentrations, and depleting granulysin reduced the cytotoxicity. Granulysin in the blister fluids was a 15-kDa secretory form, and injection of it into mouse skin resulted in features mimicking SJS-TEN. Our findings demonstrate that secretory granulysin is a key molecule responsible for the disseminated keratinocyte death in SJS-TEN and highlight a mechanism for CTL- or NK cell-mediated cytotoxicity that does not require direct cellular contact.
Patients with Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) have high mortality rates. Disseminated intravascular coagulation has been reported in SJS/TEN patients. The influence of ...this lethal complication in patients with SJS/TEN is not well known.
This study aimed to investigate the risk and outcomes of disseminated intravascular coagulation in patients with SJS/TEN.
We analyzed the disseminated intravascular coagulation profiles of patients receiving a diagnosis of SJS/TEN between 2010 and 2019.
We analyzed 150 patients with SJS/TEN (75 with SJS, 22 with overlapping SJS/TEN, and 53 with TEN) and their complete disseminated intravascular coagulation profiles. Disseminated intravascular coagulation was diagnosed in 32 patients (21.3%), primarily those with TEN. It was significantly associated with systemic complications, including gastrointestinal bleeding, respiratory failure, renal failure, liver failure, infection, and bacteremia. Additionally, SJS/TEN patients with disseminated intravascular coagulation had elevated procalcitonin levels. Among patients with SJS/TEN, disseminated intravascular coagulation was associated with a greater than 10-fold increase in mortality (78.1% vs 7%).
The study limitations include small sample size and a single hospital system.
Disseminated intravascular coagulation is a potential complication of SJS/TEN and associated with higher mortality. Early recognition and appropriate management of this critical complication are important for patients with SJS/TEN.
We simulate the injection-locking performance of a 1% antireflection coated Fabry-Perot laser amplifier (AR-FPLA) and demonstrate the 2.5-Gbit/s DWDM-PON application with the directly modulated ...AR-FPLA based ONU transmitter under side-mode injection-locking condition. The effect of the AR-FPLA front-facet reflectivity on the injection locking range detuning and the Q-factor are interpreted from theoretical simulations and are experimentally characterized. A 25-channel locking capacity is reported for such a side-mode injection-locked AR-FPLA with corresponding wavelength locking range of 30 nm, the minimal requested power of -7 dBm and gain extinction ratio of <;7 dB by reducing its front-facet reflectivity to 1%. At bit rate of 2.5 Gbit/s, the BER of 10 -12 is achievable for the nearest 17 channels of AR-FPLA at receiving power of -21 dBm, and all of the 25 injection-locked channels with SMSR of > 35 dB can guarantee the BER of <; 10 -9 with their worst receiving sensitivity degrading to -19 dBm.
