Advancing precision agriculture: The potential of deep learning for cereal plant head detection
Cereal plant heads must be identified precisely and effectively in a range of agricultural
applications, including yield estimation, disease detection, and breeding. Traditional …
applications, including yield estimation, disease detection, and breeding. Traditional …
Direct and accurate feature extraction from 3D point clouds of plants using RANSAC
While point clouds hold promise for measuring the geometrical features of 3D objects, their
application to plants remains problematic. Plants are three dimensional (3D) organisms …
application to plants remains problematic. Plants are three dimensional (3D) organisms …
Automatic branch detection of jujube trees based on 3D reconstruction for dormant pruning using the deep learning-based method
Pruning is a time-consuming and labor-intensive practice for managing of dormant jujube
orchards, in which dormant pruning is still mainly dependent on manual operation …
orchards, in which dormant pruning is still mainly dependent on manual operation …
In-field rice panicles detection and growth stages recognition based on RiceRes2Net
S Tan, H Lu, J Yu, M Lan, X Hu, H Zheng… - … and electronics in …, 2023 - Elsevier
Accurate rice panicle detection and growth stages recognition are crucial steps in rice field
phenoty**. However, conventional manual characterization of rice panicles is time …
phenoty**. However, conventional manual characterization of rice panicles is time …
[HTML][HTML] Comprehensive review on 3D point cloud segmentation in plants
H Song, W Wen, S Wu, X Guo - Artificial Intelligence in Agriculture, 2025 - Elsevier
Segmentation of three-dimensional (3D) point clouds is fundamental in comprehending
unstructured structural and morphological data. It plays a critical role in research related to …
unstructured structural and morphological data. It plays a critical role in research related to …
RoseSegNet: An attention-based deep learning architecture for organ segmentation of plants
Highlights•A new 3D point-based deep learning architecture for organ segmentation of
plants.•Attention-based modules for aggregation and propagation of features.•Exploiting …
plants.•Attention-based modules for aggregation and propagation of features.•Exploiting …
Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks
Background Plant architecture can influence crop yield and quality. Manual extraction of
architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation …
architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation …
Rapid detection of wheat ears in orthophotos from unmanned aerial vehicles in fields based on YOLOX
Y Zhaosheng, L Tao, Y Tianle, J Chengxin… - Frontiers in Plant …, 2022 - frontiersin.org
Wheat ears in unmanned aerial vehicles (UAV) orthophotos are characterized by occlusion,
small targets, dense distribution, and complex backgrounds. Rapid identification of wheat …
small targets, dense distribution, and complex backgrounds. Rapid identification of wheat …
TinyML olive fruit variety classification by means of convolutional neural networks on IoT Edge devices
AM Hayajneh, S Batayneh, E Alzoubi, M Alwedyan - AgriEngineering, 2023 - mdpi.com
Machine learning (ML) within the edge internet of things (IoT) is instrumental in making
significant shifts in various industrial domains, including smart farming. To increase the …
significant shifts in various industrial domains, including smart farming. To increase the …
Generating 3D multispectral point clouds of plants with fusion of snapshot spectral and RGB-D images
P ** is important for accelerating crop breeding.
Spectral imaging that can acquire both spectral and spatial information of plants related to …
Spectral imaging that can acquire both spectral and spatial information of plants related to …