Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
6,163
result(s) for
"Insects Identification."
Sort by:
Insects
From bees to beetles, walking sticks to inchworms, kids will learn how, where, and when to spot these animals all over the United States (and how to keep a safe distance when necessary).
Book
The Greenland Entomofauna
The Greenland Entomofauna provides a richly illustrated tool for the identification of the insects, spiders, mites etc. of the country, hence enabling detailed future monitoring of range shifts of individual species.
eBook
Application of Machine Learning for Insect Monitoring in Grain Facilities
In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests in a grain storage or food facility in real time is vital to making insect control decisions. The camera captures the image of the insect and passes it to a Jetson Nano for processing. The Jetson Nano runs a trained deep-learning model to detect the presence and species of insects. With three different lighting situations: white LED light, yellow LED light, and no lighting condition, the detection results are displayed on a monitor. Validating using F1 scores and comparing the accuracy based on light sources, the system was tested with a variety of stored grain insect pests and was able to detect and classify adult cigarette beetles and warehouse beetles with acceptable accuracy. The results demonstrate that the system is an effective and affordable automated solution to insect detection. Such an automated insect detection system can help reduce pest control costs and save producers time and energy while safeguarding the quality of stored products.
Journal Article
S-ResNet: An improved ResNet neural model capable of the identification of small insects
Precise identification of crop insects is a crucial aspect of intelligent plant protection. Recently, with the development of deep learning methods, the efficiency of insect recognition has been significantly improved. However, the recognition rate of existing models for small insect targets is still insufficient for insect early warning or precise variable pesticide application. Small insects occupy less pixel information on the image, making it more difficult for the model to extract feature information.
To improve the identification accuracy of small insect targets, in this paper, we proposed S-ResNet, a model improved from the ResNet, by varying its convolution kernel. The branch of the residual structure was added and the Feature Multiplexing Module (FMM) was illustrated. Therefore, the feature expression capacity of the model was improved using feature information of different scales. Meanwhile, the Adjacent Elimination Module (AEM) was furtherly employed to eliminate the useless information in the extracted features of the model.
The training and validation results showed that the improved residual structure improved the feature extraction ability of small insect targets compared to the original model. With compare of 18, 30, or 50 layers, the S-ResNet enhanced the identification accuracy of small insect targets by 7% than that on the ResNet model with same layer depth.
Journal Article
Hidden kingdom : the insect life of Costa Rica
\"This book presents facts about Costa Rica's insects and their evolutionary history. The photographs serve as a tool to help identify the insects a visitor to Costa Rica is likely to encounter and show the morphological adaptations, survival strategies, and interlocking roles insects play in tropical ecosystems\"-- Provided by publisher.
Book
A Real-Time PCR Assay for Detecting Codling Moth Cydia pomonella on Material Intercepted at U.S. Ports of Entry—A Valuable Tool for Specimen Identification
Codling moth Cydia pomonella is well established nearly everywhere apples are grown. Due to this almost global distribution, larvae are often intercepted at U.S. ports of entry where immature specimens cannot be identified accurately to species leading to unnecessary quarantine actions. To assist with identifying intercepted C. pomonella from port inspections, we developed a probe-based real-time PCR assay to amplify the internal transcribed spacer (ITS) region 2 of C. pomonella. The assay was tested for inclusivity using 110 C. pomonella specimens from six continents. Analytical specificity was examined by testing related species intercepted at U.S. ports of entry, as well as non-targets with the same geographic distribution and host species as C. pomonella. The assay developed here identified all C. pomonella individuals correctly and produced appropriately negative results for all non-target species. These results ensure that the assay provides a rapid and accurate tool for unambiguously identifying C. pomonella among material intercepted at U.S. ports of entry. Since C. pomonella is not actionable, the ability to identify all life stages of C. pomonella conclusively will save time, effort, and money while also directing identification efforts towards species of current quarantine concern.
Journal Article
First Report of Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae) in Oklahoma
The corn leafhopper, Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae), is an invasive insect that can cause damage to maize (Zea mays L.) in two ways: by direct feeding and the transmission of several plant pathogens. Dalbulus maidis is an invasive and serious economic pest of maize that has spread from its center of origin in Mexico to the southernmost parts of the United States. Prior to 2024, corn leafhoppers had not been documented in Oklahoma, and their spread northward toward the United States corn belt is of significant concern. Here, we provide the first reports of the insect in maize in several Oklahoma counties. Insect specimens were collected at various commercial and experimental field sites by Oklahoma State University research and extension personnel. The identity of the insect species was validated through morphological and molecular taxonomy. The presence records for the corn leafhopper presented here provide valuable information for future monitoring and management efforts of this economically important pest and disease.
Journal Article