Recent Articles

Open access

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

GeoSphere-DETR: RGB-D citrus fruit detection in cluttered orchards via geometry-gated fusion and geometry-aware confidence recalibration

Accurate fruit detection in cluttered orchard environments remains challenging due to visually similar background elements such as foliage and specular highlights, which lead to high false positive...

Adopting artificial intelligence for a circular economy in the agro-industrial sector: A critical review of past achievements and future directions

The agro-industrial sector generates substantial waste, yet its transition toward circularity remains constrained by fragmented technological adoption and limited cross-stage integration. Artificial...

YOLO11-FFTDA: fusing frequency-domain enhancement and deformable attention for robust tomato pedicel segmentation

Precise detection and segmentation of tomato pedicels are critical perception tasks for automated harvesting by picking robots. Addressing challenges posed by complex orchard environments and loss of...

CaMT: Class-aware Multi-level Training Distillation for lightweight semantic segmentation in apple orchard

Accurate semantic understanding of orchard environments is essential for enabling agricultural robots to perform navigation, monitoring, and autonomous operations under limited onboard computation....

EIT-based robotic tactile sensing for kiwifruit firmness estimation with spatiotemporal fusion

Robotic grading of fruit requires reliable contact-based firmness sensing, as firmness is a key indicator of maturity and eating quality but is often difficult to infer from vision alone. This study...

Integrating SIF-derived parameters with three-band vegetation indices for quantifying maize leaf spot severity: a case study under field conditions

Leaf spot disease, as a pervasive foliar disease, has become a significant factor limiting stable grain production. Rapid, accurate remote sensing of disease status supports precision prevention and...

Depth4PH: a vision foundation model-based framework for plant height estimation in agricultural scenes

Plant height is a key 3D phenotypic trait for assessing crop growth, biomass accumulation, and lodging resistance. To overcome the practical limitations of conventional plant height measurement methods,...

Dynamic obstacle avoidance system for agricultural machinery based on multi-sensor fusion and the SAC-DWA

Farmland is an unstructured and dynamic environment, where real-time detection and avoidance of moving obstacles are essential for the safe and autonomous operation of agricultural machinery. The Dynamic...

CNN-enabled roguing implement for parental-line management and purity assurance in hybrid rapeseed via a mobile field robot

Off-type plants in hybrid rapeseed compromise genetic purity, reduce seed yield and uniformity, and may jeopardize certification. Maintaining high-purity seed lots therefore requires early, reliable...

AgriGenSeg-leaf: A generative data engine for ultra-low-label leaf lesion segmentation with physiology-aware priors

Accurate segmentation of leaf lesions under field conditions is fundamental to intelligent crop health management but remains constrained by scarce annotations and large distribution shifts. In this...

From on-device vision to assisted harvesting: A lightweight real-time ripeness evaluation system for truss-harvested cherry tomatoes on augmented reality glasses

The transition from experience-driven manual harvesting to data-driven intelligent management represents a pivotal shift in modern facility agriculture. However, the widespread adoption of fully autonomous...

Hyperspectral–machine learning framework enables early and non-destructive prediction of plant resistance to pest

The brown planthopper (Nilaparvata lugens) is one of the most destructive pests of rice and poses a threat to yield stability and food security. Although host-plant resistance is the most sustainable...

Pest-MoE: A mixture-of-experts framework for stage-aware multimodal few-shot pest recognition

Pest recognition is vital for safeguarding plant health and maintaining ecological balance. Although mobile devices and deep learning offer promising in-field monitoring, two challenges remain: (1)...

Real-time onboard rice lodging direction regression on combine harvesters via physics-prior-guided vision mamba

Rice lodging seriously hinders the working efficiency of combine harvesters, and it often causes mechanical blockage and harvest loss. It is very important for the combine harvester to perceive the...

Diurnal cross-temporal features from UAV multispectral and thermal imagery enhance foxtail millet yield prediction accuracy under different irrigation regimes

Accurate prediction of foxtail millet yield is essential for effective field management and high-throughput breeding. Despite advances in UAV-based yield prediction for major crops, existing studies...

Integrating machine learning and large language models to enhance risk assessment and priority management for plant biosecurity

Global food security and biosecurity are continually threatened by the prevalence and spread of plant pests with the ever-accelerating rate of global trade. The continuous advancement of artificial...

Point transformer-based 3D segmentation and phenotypic trait analysis of cotton plants with foliage

High-resolution point clouds provide detailed three-dimensional (3D) spatial information about cotton plants, making them valuable for analyzing complex architectural traits. However, segmentation of...

Visual–tactile fusion for real-time weed detection in maize: Lightweight precision weed detection network with tactile-triggered keyframes

Weeds severely threaten maize growth and yield, and accurate weed detection is essential but remains challenging for precision maize protection. Although vision-based methods for weed detection have...

Target-oriented spectral disentanglement improves cross-year robustness of UAV hyperspectral inversion of winter wheat agronomic parameters

The hyperspectral reflectance of winter wheat canopy is jointly influenced by multiple agronomic parameters, including canopy structure, pigment status, and biomass accumulation. Shared spectral responses...

BaciCausalLM: A lightweight causal-reasoning large language model for Bacillus-based agricultural biomanufacturing

The genus Bacillus provides important microbial cell factories for bioactive metabolite production in agricultural biomanufacturing, yet optimization of metabolic regulation and fermentation processes...

Virtual class-aware orthogonal and contrastive prototype calibration for few-shot class-incremental plant disease identification

Deep neural networks (DNNs), renowned for their powerful representational capabilities, have achieved state-of-the-art performance in plant disease identification. However, these models are predominantly...

Contrastive learning with sparsely annotated dataset for apple detection in smart orchard farming

Detecting and locating apples are important for picking robots and orchard management. Although fully-supervised object detection (FSOD) methods have achieved impressive apple detection performance,...

Defect-aware dual-stream RGB-D perception for tomato pedicel cutting-point detection and 6D pose estimation in a robotic harvesting system

Accurate 3D localization and pose estimation of tomato pedicel shearing points are prerequisites for stable, damage-free robotic harvesting in cluttered greenhouses. However, many existing pipelines...

FCMamba: A frequency-enhanced state space network for edge-based plant disease recognition

Accurate plant disease recognition on edge devices requires models that can preserve fine-grained lesion details, capture long-range spatial dependencies, and maintain low computational cost. However,...

PII-CNN-LSTM: A multi-modal deep learning framework integrating novel pollination importance index for predicting optimal apple pollination windows

Pollination optimization in apple orchards faces increasing challenges from climate variability and declining pollinator populations, necessitating precision timing strategies. This study introduces...

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