Recent Articles

Open access

ISSN: 2589-7217
CN: 10-1795/S
p-ISSN: 2097-2113

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

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...

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

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...

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

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...

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

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...

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

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...

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

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...

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

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...

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

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...

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

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...

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

Positioning accuracy directly affects the operational performance and stability of navigation systems. However, in complex field environments, severe vibrations during combine harvester operation can...

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

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...

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

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...

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

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,...

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

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...

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

Robotic apple harvesting has gradually become a critical requirement for modern agriculture. However, complex orchard environments and limited computational resources pose significant challenges for...

YOLOSc-SAM: An acceptable extraction method for farmland regions in remote sensing images using SAM

Farmland is a core spatial unit in agricultural production management, and accurately delineating farmland boundaries is of great significance for agricultural resource surveys and cropland monitoring....

Spatial pose estimation of apples for robotic harvesting

In the apple picking process, the existing robots are unable to accurately estimate the pose of apples. This often leads to damage to the fruits, branches, and even the robot fingers during grasping,...

Decoupling canopy structure effects from vegetation indices for robust assessment of potato plant nitrogen content across growth stages

Accurate monitoring of plant nitrogen concentration (PNC) is essential for precision nitrogen (N) fertilization management at the farm scale. While numerous vegetation indices (VIs) have been proposed...

A hierarchical framework for cotton boll opening rate monitoring using UAV RGB imagery

The cotton boll opening rate (CBOR) is a critical agronomic indicator determining yield and fiber quality. This study developed a hierarchical framework integrating unmanned aerial vehicle (UAV) RGB...

From pixels to points: An AI framework with weaker-and-fewer-labels for lightweight 3D phenotyping using 2D-3D coordinate mapping and VLMs

3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation...

LUNet: Language-infused UNet for precise Camellia oleifera pests and diseases segmentation in complex agricultural environment

With the rapid development of deep learning, precise segmentation of Camellia oleifera pests and diseases has become a pivotal technical bottleneck in crop health monitoring and intelligent plant protection...

AI-based respiratory rate estimation in group-housed pigs infected with Mycoplasma hyopneumoniae under occlusion conditions

Respiratory rate (RR) is a critical physiological indicator for assessing the health and welfare of pigs. Although numerous studies have focused on RR monitoring, most have overlooked the issue of mutual...

Integrative hormone-metabolite tracking reveals ultra-early immune responses to anthracnose in tea cultivars

Plant fungal pathogens, particularly anthracnose, pose a significant threat to tea production by adversely affecting both yield and quality. Detecting these pathogens has been challenging due to the...

Soil knowledge-guided multi-task transformer model for predicting soil properties and crop traits with early warning alerts

Soil properties are vital for soil health, fertility, and crop productivity, yet traditional methods like the Partial Least Squares Regression (PLSR) predict individual properties without capturing...

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