294 results

Adaptive compensation method for navigation positioning errors considering the vibration characteristics of a combine harvester

Wenbo Wei; Hui Wang; Maohua Xiao et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Positioning accuracy directly affects the operational performance and stability of navigation systems. However, in complex field environments, severe vibrations during combine harvester operation can significantly exacerbate positioning errors. These vibrations have become a key limiting factor for…

CTGNN: UAV-satellite cross-domain transfer learning for monitoring oat growth in China’s key production areas

Pengpeng Zhang; Bing Lu; Jiali Shang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Modern agricultural production necessitates real-time, precise monitoring of crop growth status to optimize management decisions. While remote sensing technologies offer multi-scale observational capabilities, conventional crop monitoring models face two critical limitations: (1) the independent…

Corrigendum regarding updated declaration of competing interest statement in previously published articles

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Maize phenological stage recognition via coordinated UAV and UGV multi-view sensing and deep learning

Jibo Yue; Haikuan Feng; Yiguang Fan et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Crop phenological stages, marked by key events such as germination, leaf emergence, flowering, and senescence, are critical indicators of crop development. Accurate, dynamic monitoring of these stages is essential for crop breeding management. This study introduces a novel multi-view sensing…

LPNet: A lightweight progressive network for calyx-aware apple pose estimation in orchard environments

Wenbei Wang; Licheng Zhu; Bo Zhao et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Robotic apple harvesting has gradually become a critical requirement for modern agriculture. However, complex orchard environments and limited computational resources pose significant challenges for achieving accurate and efficient vision-based pose estimation of apples. To address these…

SpecColorNet: An interpretable multimodal deep learning approach for predicting SSC of multiple pears

Xin Xu; Mengfei Yang; Jie Yang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Visible/near-infrared spectroscopy provides a non-destructive approach for evaluating soluble solids content (SSC) of pears (Pyrus pyrifolia Nakai). However, variations among pear cultivars, especially in pear color, markedly affect spectral reflectance, thereby limiting the cross-cultivar SSC…

PlantSegNeRF: A few-shot, cross-species method for plant 3D instance point cloud reconstruction via joint-channel NeRF with multi-view image instance matching

Xin Yang; Ruiming Du; Hanyang Huang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Organ segmentation of plant point clouds is a prerequisite for the high-resolution and accurate extraction of organ-level phenotypic traits. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, the existing techniques for organ segmentation…

Optimizing herbicide reduction: A simulation approach using Artificial Intelligence and different nozzle configurations

Renato H. Furlanetto; Ana C. Buzanini; Arnold W. Schumann et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Targeted application aims to minimize product usage by spraying only where needed. However, there is a lack of tools to evaluate the potential savings and the model's limitations. To address this, we developed a computer simulation using two YOLOv8x models (bounding boxes and segmentation masks) to…

SCLFormer: A synergistic convolution-linear attention transformer for hyperspectral image classification of mechanical damage in maize kernels

Yiqiang Zheng; Jun Fu; Fengshuang Liu et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Classifying mechanical damage in maize kernels using hyperspectral imaging is crucial for food security and loss reduction. Existing methods are constrained by high computational complexity and limited precision in detecting subtle damages, such as pericarp damage and kernel cracks. To address…

Multi-scale feature alignment network for 19-class semantic segmentation in agricultural environments

Zhi-xin Yao; Hao Wang; Zhi-jun Meng et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

To improve environmental perception and ensure reliable agricultural machinery navigation during field transitions under unstructured farm road conditions, this study utilizes high-resolution RGB camera vision navigation technology to propose a Multi-Scale Feature Alignment Network (MSFA-Net) for…

Integrating 3D detection networks and dynamic temporal phenotyping for wheat yield classification and prediction

Honghao Zhou; Bingxi Qin; Qing Li et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Automated phenotyping of wheat growth stages from 3D point clouds is still limited. The study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping. A novel 3D wheat plot detection network—integrating…

Prediction of wheat stem biomass using a new unified model driven by phenological variable under remote-sensed canopy vegetation index constraints

Weinan Chen; Guijun Yang; Yang Meng et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Timely and accurate prediction of stem dry biomass (SDB) is crucial for monitoring crop growing status. However, conventional biomass estimation models are often limited by the influence of crop growth phase, which significantly restricts their temporal and spatial transferability. This study aimed…

Multi-arm robotic system and strategy for the automatic packaging of apples

Yizhi Zhang; Liping Chen; Xin Li et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Packaging is a crucial step in the commercial distribution of apples after harvest. However, achieving fast and accurate automated packaging remains a challenge. This study proposed for the first time a highly integrated four-arm robotic system and an effective implementation strategy specifically…

Hyperspectral estimation of leaf chlorophyll content in wine grape using transfer learning and three-dimensional radiation transfer model

Qifan Chen; Zhiwen Mi; Qifan Wang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Accurate and timely monitoring of leaf chlorophyll content (LCC) in wine grapes is essential for assessing their photosynthetic status, optimizing management and planting practices, and improving both yield and quality. Traditional physically-based and empirically-based estimation methods often…

Dual attention guided context-aware feature learning for residual unfilled grains detection on threshed rice panicles

Yuhao Zhou; Xiao Feng; Shuqi Tang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Accurate detection of residual unfilled grains on threshed rice panicles is a critical step in determining a reliable grain-setting rate, and holds significant potential for the development of high-quality rice strains. Recent deep learning-based techniques have been actively explored for…

Decoupling tea-bud heap structure from non-imaging hyperspectral spectra for accurate single-bud trace biochemistry retrieval

Ning Qi; Hao Yang; Jianbo Qi et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Accurate, real-time, non-destructive estimation of single-bud biochemistry is critical for managing green-tea quality, yet non-imaging hyperspectral measurements of arbitrarily stacked buds are confounded by structured interference from mixed adaxial/abaxial surfaces and their three-dimensional…

Energy-saving and stability-enhancing control for unmanned distributed drive electric plant protection vehicle based on active torque distribution

Wenxiang Xu; Yejun Zhu; Maohua Xiao et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

The distributed drive electric plant protection vehicle (DDEPPV), equipped with a unique four-wheel independent drive system, demonstrates excellent path-tracking capability and dynamic performance in agricultural environments. However, during actual field operations, issues such as severe tire…

End-to-end detection of cough and snore based on ResNet18-TF for breeder laying hens: A field study

Haoyan Ma; Peiguang Xin; Juncheng Ma et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Cough and snore are the most representative vocalizations for chicken respiratory diseases, which severely restrict poultry health due to highly contagious and lethal characteristics. Nighttime inspection by veterinarians is the foremost solution to identify bird respiratory symptoms during…

Improved YOLOv8 for multi-colored apple fruit instance segmentation and 3D localization

Jiaren Zhou; Mengyan Chen; Man Zhang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Robotic apple harvesting requires precise instance segmentation and 3D localization, especially for multi-colored apples under complex orchard conditions with occlusions and variable lighting. Current deep learning methods lack robustness and accuracy for such scenarios, limiting automation. This…

Advancing lightweight and efficient detection of tomato main stems for edge device deployment

Guohua Gao; Lifa Fang; Zihua Zhang et al.

Artificial Intelligence in Agriculture, Vol. 16 No. 1 · 2026 · English · DOAJ Scopus

Automated pruning and defoliation of tomato plants are essential in modern cultivation systems for optimizing canopy structure, enhancing air circulation, and increasing yield. However, detecting main stems in the field faces significant challenges, like complex background interference, limited…