Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile devices for detecting corn pests. We propose a two-step transfer learning approach to enhance the accuracy of two versions of the MobileNet SSD network. Five beetle species (Coleoptera), including four harmful to corn crops (belonging to genera Anoxia, Diabrotica, Opatrum and Zabrus), and one beneficial (Coccinella sp.), were selected for preliminary testing. We employed two datasets. One for the first transfer learning procedure comprises 2605 images with general dataset classes ‘Beetle’ and ‘Ladybug’. It was used to recalibrate the networks’ trainable parameters for these two broader classes. Furthermore, the models were retrained on a second dataset of 2648 images of the five selected species. Performance was compared with a baseline model in terms of average accuracy per class and mean average precision (mAP). MobileNet-SSD-v2-Lite achieved an mAP of 0.8923, ranking second but close to the highest mAP (0.908) obtained by MobileNet-SSD-v1 and outperforming the baseline mAP by 6.06%. It demonstrated the highest accuracy for Opatrum (0.9514) and Diabrotica (0.8066). Anoxia it reached a third-place accuracy (0.9851), close to the top value of 0.9912. Zabrus achieved the second position (0.9053), while Coccinella was reliably distinguished from all other species, with an accuracy of 0.8939 and zero false positives; moreover, no pest species were mistakenly identified as Coccinella. Analyzing the errors in the MobileNet-SSD-v2-Lite model revealed good overall accuracy despite the reduced size of the training set, with one misclassification, 33 non-identifications, 7 double identifications and 1 false positive across the 266 images from the test set, yielding an overall relative error rate of 0.1579. The preliminary findings validated the two-step transfer learning procedure and placed the MobileNet-SSD-v2-Lite in the first place, showing high potential for using neural networks on real-time pest control while protecting beneficial species.

Precision Corn Pest Detection: Two-Step Transfer Learning for Beetles (Coleoptera) with MobileNet-SSD

Coccinella septempunctata (ladybird) is an extremely important natural predator that feeds on aphids. An assessment of the toxicity of pesticides on environmental organisms is an essential component of Integrated Pest Management (IPM) strategies. This study evaluated diamide insecticides’ toxicity at lethal and 30% lethal doses (LR30) against C. septempunctata larvae. The pre-imaginal median lethal doses (LR50) of chlorantraniliprole 10% SC, tetrachlorantraniliprole 10% SC, and broflanilide 10% SC were calculated to be 42.078, 289.516, and 0.0943 g active ingredient (a.i.)/ha, respectively. The mortality tests demonstrated that chlorantraniliprole and tetrachlorantraniliprole are comparatively less toxic to C. septempunctata than broflanilide, which were detected to be highly toxic to C. septempunctata. The mortality rates of the groups treated with the three diamide insecticides tended to stabilize after 96 h, extending to the pre-imaginal stage. Furthermore, when compared to broflanilide, which had a much higher potential risk, the hazard quotient (HQ) values indicated that chlorantraniliprole and tetrachlorantraniliprole have a lower risk potential for C. septempunctata in farmland and off farmland. The LR30 dose induces abnormalities in the development phase 4th-instar larvae weight, pupal weight, and adult weight of treated C. septempunctata. The study emphasizes the importance of assessing the adverse effects of diamide insecticides on natural predator species that serve as biological control agents in agricultural IPM strategies.

/pdf/toxicity-and-sublethal-effects-of-diamide-insecticides-on-17n818zw.pdf

Toxicity and Sublethal Effects of Diamide Insecticides on Key Non-Target Natural Predators, the Larvae of Coccinella septempunctata L. (Coleoptera: Coccinellidae)

How exposure to a neonicotinoid pesticide affects innate and learned close-range foraging behaviour of a classical biological control agent

El maíz es considerado el tercer cultivo de grano más importante del mundo debido a su interés económico, alimentario e industrial. A lo largo de su fenología este cultivo es atacado por plagas y enfermedades, que pueden generar efectos adversos sobre la producción. Para el control de estos problemas, generalmente se acude al manejo convencional, los cuales ocasionan desequilibrios ecológicos. Por esta razón, es necesario considerar alternativas sostenibles fundamentadas en el conocimiento de la entomofauna benéfica. Esta investigación tuvo por objetivo realizar un análisis prospectivo de los coccinélidos asociados al cultivo de maíz en el cantón Santa Ana, Ecuador. Para su efecto, se evaluaron los coccinélidos presentes en dos condiciones distintas: con y sin aplicación de insecticida. Se utilizaron cinco técnicas de muestreo, de acuerdo con el protocolo de investigación, y se procedió con la clasificación taxonómica. Los datos fueron interpretados mediante estadística descriptiva; además, se valoró la diversidad alfa y beta. En el estudio se encontraron 297 individuos, congregados a: 1 orden,1 familia, 13 géneros y 11 especies. La mayor abundancia se presentó en condiciones no intervenidas, determinándose que la técnica de trampeo más eficiente para su captura fueron las trampas cromáticas. Este tipo de investigación es importante para derivar programas de control biológico en cultivos de importancia económica. 

pdf/prospeccion-de-los-coccinelidos-asociados-al-cultivo-de-maiz-2amssbdp2i.pdf

Prospección de los coccinélidos asociados al cultivo de maíz (Zea mays L.)

Selection of novel ssDNA aptamer for ultra-sensitive colorimetric detection of acetamiprid using ultra-small dot assay

Coccinella septempunctata is a nontarget beneficial arthropod and an important aphid predator in agricultural crops. In this study, the toxic effects of the neonicotinoid acetamiprid on C. septempunctata were investigated to determine its applicability and efficacy against the aphid predator. The results of the toxicity test showed that the second instar larvae of C. septempunctata were the most sensitive to acetamiprid. The LC50 values of the 1st, 2nd, 3rd, and 4th instar larvae were 15.767, 9.412, 18.850, and 25.278 mg a.i. L−1, respectively. Compared with that of the control, the predation ability of different larval instars was inhibited by sublethal concentrations of acetamiprid. The results of the predatory function test showed that sublethal concentrations of acetamiprid could reduce the consumption of aphids by fourth instar C. septempunctata larvae over a short duration and significantly inhibited the predatory ability of ladybird larvae. The results of the developmental test showed that sublethal concentration of acetamiprid shortened the growth duration of C. septempunctata larvae. Acetamiprid had considerable adverse effects on the different developmental stages of C. septempunctata. Together, our results provide information for implementation in biological and chemical control strategies for the integrated management of aphids.

/pdf/the-toxicity-response-of-coccinella-septempunctata-l-5sr46y7e.pdf

The Toxicity Response of Coccinella septempunctata L. (Coleoptera: Coccinellidae) after Exposure to Sublethal Concentrations of Acetamiprid

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Fengqiu Luo

Author Tools

Chat about Author

Papers

The Toxicity Response of Coccinella septempunctata L. (Coleoptera: Coccinellidae) after Exposure to Sublethal Concentrations of Acetamiprid