MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals

D Zhang, H Li, J Xie, D Li - Neural Networks, 2023 - Elsevier
Non-stationarity of EEG signals leads to high variability between subjects, making it
challenging to directly use data from other subjects (source domain) for the classifier in the …

AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning

W Zhang, H Huang, Y Sun, X Wu - Frontiers in Plant Science, 2022 - frontiersin.org
Light traps have been widely used for automatic monitoring of pests in the field as an
alternative to time-consuming and labor-intensive manual investigations. However, the scale …

A multi-species pest recognition and counting method based on a density map in the greenhouse

Z Zhang, J Rong, Z Qi, Y Yang, X Zheng, J Gao… - … and Electronics in …, 2024 - Elsevier
Whiteflies and fruit flies are common pests that adversely affect greenhouse crops, so it is
vital to control their numbers promptly. Current research involves setting up artificial …

An intelligent system for high-density small target pest identification and infestation level determination based on an improved YOLOv5 model

L Sun, Z Cai, K Liang, Y Wang, W Zeng… - Expert Systems with …, 2024 - Elsevier
Purpose: A deep learning-based intelligent system has been developed for the identification
and detection of high-density small target pests with the aim of addressing the limitations …

SLCOBNet: Shrimp larvae counting network with overlapping splitting and Bayesian-DM-count loss

Y Qu, S Jiang, D Li, P Zhong, Z Shen - Biosystems Engineering, 2024 - Elsevier
Estimating the number of shrimp larvae plays a critical role for achieving reasonable feeding
in aquaculture. However, previous shrimp larvae counting models failed to accurately …

Feature enhancement guided network for yield estimation of high-density jujube

F Cheng, J Wei, S Jiang, Q Chen, Y Ru, H Zhou - Plant Methods, 2023 - Springer
Background Automatic and precise jujube yield prediction is important for the management
of orchards and the allocation of resources. Traditional yield prediction techniques are …

Automatic detection and counting of planthoppers on white flat plate images captured by AR glasses for planthopper field survey

H Sheng, Q Yao, J Luo, Y Liu, X Chen, Z Ye… - … and Electronics in …, 2024 - Elsevier
Rice planthoppers usually refer to brown planthoppers (Nilaparvata lugens), white-backed
planthoppers (Sogatella furcifera), and small brown planthoppers (Laodelphax striatellus) …

Computer vision model for sorghum aphid detection using deep learning

I Grijalva, BJ Spiesman, B McCornack - Journal of Agriculture and Food …, 2023 - Elsevier
Aphids are a challenging crop pest to manage. The sorghum aphid, for example, causes
considerable yield loss in unmanaged sorghum. One of the key strategies to mitigate yield …

High-throughput image-based plant stand count estimation using convolutional neural networks

S Khaki, H Pham, Z Khalilzadeh, A Masoud, N Safaei… - Plos one, 2022 - journals.plos.org
The landscape of farming and plant breeding is rapidly transforming due to the complex
requirements of our world. The explosion of collectible data has started a revolution in …

A density map regression method and its application in the coal flotation froth image analysis

Y Fan, Z Lv, W Wang, R Tian, K Zhang, M Wang… - Measurement, 2022 - Elsevier
The surface feature of flotation froth is an indicator of the flotation process state. Image-
based methods have long been considered as an indirect detector to access flotation …