Results 1 to 10 of about 3,675,679 (239)

Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module

Sensors, 2023
Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a ...
Qiuchi Xiang, Xiaoning Huang, Zhouxu Huang, Xingming Chen, Jintao Cheng, Xiaoyu Tang   +5 more
doaj   +4 more sources

YOLO-PEST: a novel rice pest detection approach based on YOLOv5s

Plant Methods
In rice pest management, accurate pest detection is critical for intelligent agricultural systems, yet challenges like limited dataset availability, pest occlusion, and insufficient small object detection accuracy hinder effective monitoring.
Jun Qiang, Li Zhao, Hongming Wang, Tianqi Xu, Qihang Jia, Lixiang Sun   +5 more
doaj   +3 more sources

Crop pest detection by three-scale convolutional neural network with attention.

PLoS ONE, 2023
Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy.
Xuqi Wang, Shanwen Zhang, Xianfeng Wang, Cong Xu   +3 more
doaj   +2 more sources

Antioxidant processes involving epicatechin decreased symptoms of pine wilt disease

Frontiers in Plant Science, 2022
Since the pine wood nematode (PWN, Bursaphelenchus xylophilus) invasion of Northeast China, both symptomatic and asymptomatic PWN carriers have been found. Asymptomatic PWN carriers, which are more dangerous than symptomatic carriers, constitute a source
Ruizhi Zhang, Jianan Wang, Rui Xia, Danlei Li, Feng Wang, Feng Wang   +5 more
doaj   +1 more source

Alternative Tissue Sampling for Improved Detection of Candidatus Liberibacter asiaticus

Plants, 2023
Early detection and prompt response are key factors in the eradication of ‘huanglongbing’ (HLB) in California. Currently, qPCR testing of leaf tissue guides the removal of infected trees.
Subhas Hajeri, Sandra Olkowski, Lucita Kumagai, Neil McRoberts, Raymond K. Yokomi   +4 more
doaj   +1 more source

Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection

Frontiers in Plant Science, 2022
An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM).
Lin Jiao, Lin Jiao, Gaoqiang Li, Peng Chen, Peng Chen, Rujing Wang, Rujing Wang, Rujing Wang, Jianming Du, Haiyun Liu, Haiyun Liu, Shifeng Dong, Shifeng Dong   +12 more
doaj   +1 more source

Prior knowledge auxiliary for few-shot pest detection in the wild

Frontiers in Plant Science, 2023
One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive.
Xiaodong Wang, Xiaodong Wang, Jianming Du, Chengjun Xie, Shilian Wu, Xiao Ma, Kang Liu, Shifeng Dong, Shifeng Dong, Tianjiao Chen, Tianjiao Chen   +10 more
doaj   +1 more source

Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8

Journal of Agricultural Sciences
Tomato (Solanum lycopersicum L.) is vital for global nutrition and economic stability, yet it is threatened by pests such as Tuta absoluta, Helicoverpa armigera, and Bemisia tabaci. Effective pest management is crucial to prevent significant crop losses.
Yavuz Selim Şahin, Hasan Şahin, Nimet Sema Gençer   +2 more
doaj   +4 more sources

A review of pest surveillance techniques for detecting quarantine pests in Europe [PDF]

EPPO Bulletin, 2012
This paper provides reviews of the most commonly used methods to detect plant pests belonging to groups of invasive organisms with high economic relevance, including Coleoptera (bark beetles, flathead borers, leaf beetles, longhorn beetles, weevils), Diptera (cone and seed flies, fruit flies), Homoptera (aphids, leafhoppers and psyllids, whiteflies ...
Augustin S., Boonham N., De Kogel W. J., Donner P., FACCOLI, MASSIMO, Lees D. C., MARINI, LORENZO, MORI, NICOLA, PETRUCCO TOFFOLO, EDOARDO, Quilici S., Roques A., Yart A., BATTISTI, ANDREA   +12 more
openaire   +7 more sources

MSR-RCNN: A Multi-Class Crop Pest Detection Network Based on a Multi-Scale Super-Resolution Feature Enhancement Module

Frontiers in Plant Science, 2022
Pest disaster severely reduces crop yield and recognizing them remains a challenging research topic. Existing methods have not fully considered the pest disaster characteristics including object distribution and position requirement, leading to ...
Yue Teng, Yue Teng, Jie Zhang, Shifeng Dong, Shifeng Dong, Shijian Zheng, Liu Liu   +6 more
doaj   +1 more source