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Heterogeneous Features and Deep Learning Networks Fusion-Based Pest Detection, Prevention and Controlling System Using Iot and Pest Sound Analytics in a Wide Agriculture System

https://doi.org/10.2139/ssrn.4644422
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Early‐season plant cover supports more effective pest control than insecticide applications
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Pest prevention, risk, and risk management: The case of Drosophila suzukii
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EU microbial pest control: A revolution in waiting
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Automated detection of the yellow‐legged hornet (Vespa velutina) using an optical sensor with machine learning
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Magnetic Hydrogel Microrobots as Insecticide Carriers for In Vivo Insect Pest Control in Plants
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Social Concerns and Regulation of Cisgenic Crops in North America
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Sustainable Agriculture Through Technological Innovations
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AI for Sustainable Agriculture and Rangeland Monitoring
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Machine Learning Approach-Based Plant Disease Detection and Pest Detection System
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Agricultural Insect Pest’s Recognition System Using Deep Learning Model
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The role of insect intestinal microbes in controlling of Empoasca onukii Matsuda (Hemiptera: Cicadellidae) pest infestations in the production of tea garden: a review
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Bio Prospecting of Endophytes and PGPRs in Artemisinin Production for the Socio-economic Advancement
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Machine learning ensemble with image processing for pest identification and classification in field crops
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Multi-feature decision fusion algorithm for disease detection on crop surface based on machine vision
inferredcites →https://doi.org/10.1007/s10071-022-01731-4openalex-extracted
Clever pest control? The role of cognition in biological pest regulation
inferredcites →https://doi.org/10.1007/s10340-021-01422-yopenalex-extracted
Nanocarrier-mediated transdermal dsRNA-NPF1 delivery system contributes to pest control via inhibiting feeding behavior in Grapholita molesta
inferredcites →https://doi.org/10.1007/s10653-023-01668-8openalex-extracted
Application of pesticide in paddy fields: a Southeast Asia case study review
inferredcites →https://doi.org/10.1007/s11042-022-13673-7openalex-extracted
Deep learning based automated disease detection and pest classification in Indian mung bean
inferredcites →https://doi.org/10.1007/s11042-023-16476-6openalex-extracted
Design of inception with deep convolutional neural network based fall detection and classification model
inferredcites →https://doi.org/10.1007/s11042-023-16680-4openalex-extracted
MAM-IncNet: an end-to-end deep learning detector for Camellia pest recognition
inferredcites →https://doi.org/10.1007/s11119-023-10020-0openalex-extracted
An ultra-lightweight efficient network for image-based plant disease and pest infection detection
inferredcites →https://doi.org/10.1007/s13593-023-00870-3openalex-extracted
How agricultural techniques mediating bottom-up and top-down regulation foster crop protection against pests. A review
inferredcites →https://doi.org/10.1007/s13744-022-01021-1openalex-extracted
Challenges in the Biological Control of Pests in Poultry Production: a Critical Review of Advances in Brazil
inferredcites →https://doi.org/10.1007/s13762-022-04032-yopenalex-extracted
Impacts of the use of biological pest control on the technical efficiency of the Brazilian agricultural sector
inferredcites →https://doi.org/10.1016/j.aac.2022.10.001openalex-extracted
Understanding the potential applications of Artificial Intelligence in Agriculture Sector
inferredcites →https://doi.org/10.1016/j.aej.2023.08.027openalex-extracted
Integrating blockchain and deep learning for intelligent greenhouse control and traceability
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Fruit fly automatic detection and monitoring techniques: A review
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A comprehensive analysis of the advances in Indian Digital Agricultural architecture
inferredcites →https://doi.org/10.1016/j.bspc.2023.104710openalex-extracted
Pest detection via hybrid classification model with fuzzy C-means segmentation and proposed texture feature
inferredcites →https://doi.org/10.1016/j.compag.2022.107181openalex-extracted
Empirical mode decomposition applied to acoustic detection of a cicadid pest
inferredcites →https://doi.org/10.1016/j.compag.2023.107694openalex-extracted
Field detection of small pests through stochastic gradient descent with genetic algorithm
inferredcites →https://doi.org/10.1016/j.compag.2023.108079openalex-extracted
Recognizing sounds of Red Palm Weevils (RPW) based on the VGGish model: Transfer learning methodology
inferredcites →https://doi.org/10.1016/j.compag.2023.108154openalex-extracted
Early detection of red palm weevil infestations using deep learning classification of acoustic signals
inferredcites →https://doi.org/10.1016/j.compag.2023.108158openalex-extracted
Detection of citrus pests in double backbone network based on single shot multibox detector
inferredcites →https://doi.org/10.1016/j.compag.2023.108228openalex-extracted
YOLO-based detection of Halyomorpha halys in orchards using RGB cameras and drones
inferredcites →https://doi.org/10.1016/j.compag.2023.108233openalex-extracted
MD-YOLO: Multi-scale Dense YOLO for small target pest detection
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.106611openalex-extracted
Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique
inferredcites →https://doi.org/10.1016/j.compeleceng.2023.108799openalex-extracted
Smart and sustainable agriculture: Fundamentals, enabling technologies, and future directions
inferredcites →https://doi.org/10.1016/j.eap.2022.12.003openalex-extracted
Economic consequences of climate change impacts on the agricultural sector of South Asia: A case study of Sri Lanka
inferredcites →https://doi.org/10.1016/j.ecoinf.2023.102217openalex-extracted
A review of deep learning techniques used in agriculture
inferredcites →https://doi.org/10.1016/j.ecoinf.2023.102274openalex-extracted
A deep learning-based approach for bee sound identification
inferredcites →https://doi.org/10.1016/j.engappai.2023.105985openalex-extracted
IoT-based pest detection and classification using deep features with enhanced deep learning strategies
inferredcites →https://doi.org/10.1016/j.indcrop.2023.117220openalex-extracted
Non-food crops derived lignocellulose biorefinery for sustainable production of biomaterials, biochemicals and bioenergy: A review on trends and techniques
inferredcites →https://doi.org/10.1016/j.jclepro.2023.137208openalex-extracted
Industry 4.0 digital technologies enhancing sustainability: Applications and barriers from the agricultural industry in an emerging economy
inferredcites →https://doi.org/10.1016/j.jclepro.2023.138900openalex-extracted
Blockchain-enabled smart agriculture: Enhancing data-driven decision making and ensuring food security
inferredcites →https://doi.org/10.1016/j.procs.2023.01.208openalex-extracted
Exploring Deep Ensemble Model for Insect and Pest Detection from Images
inferredcites →https://doi.org/10.1051/e3sconf/202338705003openalex-extracted
AI based pest detection and alert system for farmers using IoT
inferredcites →https://doi.org/10.1108/jadee-07-2020-0140openalex-extracted
Artificial intelligence in agricultural value chain: review and future directions
inferredcites →https://doi.org/10.1109/access.2019.2949852openalex-extracted
An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field
inferredcites →https://doi.org/10.1109/access.2020.3024891openalex-extracted
An AIoT Based Smart Agricultural System for Pests Detection
inferredcites →https://doi.org/10.1109/access.2020.3025325openalex-extracted
Research on Recognition Model of Crop Diseases and Insect Pests Based on Deep Learning in Harsh Environments
inferredcites →https://doi.org/10.1109/access.2023.3317506openalex-extracted
Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification
inferredcites →https://doi.org/10.1109/icaaic56838.2023.10141014openalex-extracted
Design of Rat Trap based on Ultrasonic Waves using Bluetooth Technology
inferredcites →https://doi.org/10.1109/iciccs56967.2023.10142937openalex-extracted
Supervised Deep Learning based Leaf Disease and Pest Detection using Image Processing
inferredcites →https://doi.org/10.1109/icirca57980.2023.10220654openalex-extracted
Survey on a Drone Based Insect Repellent System Using IoT and ML
inferredcites →https://doi.org/10.1109/indin41052.2019.8972026openalex-extracted
Deep Learning based Automatic Approach using Hybrid Global and Local Activated Features towards Large-scale Multi-class Pest Monitoring
inferredcites →https://doi.org/10.1109/kse.2018.8573422openalex-extracted
Shallow and Deep Learning Architecture for Pests Identification on Pomelo Leaf
inferredcites →https://doi.org/10.1109/sustech57309.2023.10129631openalex-extracted
PEDS-AI: A Novel Unmanned Aerial Vehicle Based Artificial Intelligence Powered Visual-Acoustic Pest Early Detection and Identification System for Field Deployment and Surveillance
inferredcites →https://doi.org/10.1117/1.jei.32.5.052403openalex-extracted
Data-driven few-shot crop pest detection based on object pyramid for smart agriculture
inferredcites →https://doi.org/10.1155/2023/4213645openalex-extracted
Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform
inferredcites →https://doi.org/10.1186/s13638-019-1613-zopenalex-extracted
Experimental analysis and evaluation of wide residual networks based agricultural disease identification in smart agriculture system
inferredcites →https://doi.org/10.1371/journal.pone.0276456openalex-extracted
Crop pest detection by three-scale convolutional neural network with attention
inferredcites →https://doi.org/10.1371/journal.pone.0286732openalex-extracted
Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection
inferredcites →https://doi.org/10.3389/fphys.2023.1238111openalex-extracted
Fitness costs of resistance to insecticides in insects
inferredcites →https://doi.org/10.3389/fpls.2023.1102594openalex-extracted
The use of selenium for controlling plant fungal diseases and insect pests
inferredcites →https://doi.org/10.3389/fpls.2023.1239594openalex-extracted
Smart high-yield tomato cultivation: precision irrigation system using the Internet of Things
inferredcites →https://doi.org/10.3390/agronomy13020410openalex-extracted
Based on FCN and DenseNet Framework for the Research of Rice Pest Identification Methods
inferredcites →https://doi.org/10.3390/agronomy13071779openalex-extracted
A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s
inferredcites →https://doi.org/10.3390/agronomy13082139openalex-extracted
Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone
inferredcites →https://doi.org/10.3390/app13020863openalex-extracted
Trunk Borer Identification Based on Convolutional Neural Networks
inferredcites →https://doi.org/10.3390/app13031851openalex-extracted
IoT-Based Cotton Plant Pest Detection and Smart-Response System
inferredcites →https://doi.org/10.3390/app13052921openalex-extracted
An IoT-Based System for Efficient Detection of Cotton Pest
inferredcites →https://doi.org/10.3390/insects14010054openalex-extracted
A New Pest Detection Method Based on Improved YOLOv5m
inferredcites →https://doi.org/10.3390/s23084127openalex-extracted
A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton
inferredcites →https://doi.org/10.3390/su151511633openalex-extracted
The Impact of Natural Disasters and Pest Infestations on Technical Efficiency in Rice Production: A Study in Vietnam
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Plant disease and pest detection using deep learning-based features
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