Recent Articles

Open access

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

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...

Integrating hyperspectral radiation transfer modeling and deep transfer learning to estimate nitrogen density in winter wheat canopies

Nitrogen is a core element that regulates winter wheat yield and quality, and its precise monitoring is crucial for sustainable agricultural development. Traditional empirical methods and physical inversion...

Optimized modular transfer learning framework integrating PROSAIL and UAV-based hyperspectral reconstruction for cotton canopy water and nitrogen content retrieval

Optimizing water and fertilizer management is crucial for improving cotton yield and quality. However, reliable and generalizable models for quickly and accurately estimating cotton canopy leaves water...

MA-UQNet: A multi-modal uncertainty quantification neural network for remote sensing-based wheat aboveground biomass estimation

Accurate aboveground biomass estimation with quantified uncertainty is essential for precision agriculture, enabling risk-aware decision-making and strategic model improvement. Existing approaches predominantly...

Transformer-based cross-view LiDAR–orthomosaic fusion for geo-localization and digital modeling in apple orchards

Precision agriculture increasingly relies on accurate large-scale localization and digital modeling for autonomous tasks in orchards. However, map drift and localization uncertainty under GNSS-limited...

Optimization and evaluation of weed control performance in variable-rate patch spraying in field: Small-scale prescription operation

Weed management is an important means to ensure crop production, and the extensive use of herbicides destroys the ecological balance of farmland and enhances weed resistance. In this paper, an intelligent...

Evaluation of Multi-Object Detection models for automated goat behavior identification in intensive farming facilities

Computer vision offers significant potential for the continuous, stress-free, and cost-effective monitoring of animal behavior, yet its application in goat farming remains limited. In this study, a...

An integrated time series modeling and computer vision framework for predictive structure characterization of extruded plant-based meat products

Plant-based meat extrusion is a complex multi-stage process involving dynamic interactions among raw materials, operational parameters, and resulting product structure. Maintaining consistent product...

Multimodal remote sensing combination for maize LAI estimation: Stacking model development and phenology-specific feature sensitivity analysis

Leaf Area Index (LAI) is a key biophysical parameter for characterizing canopy structure, playing a critical role in precision agricultural monitoring and management. However, traditional optical remote...

Distributed crop disease detection with deep Q-network agent decision-making in a compressed state space

Crop disease detection is vital for reducing agricultural losses. Traditional centralized methods face challenges with data privacy and model performance. Distributed learning offers a promising alternative,...

Leveraging artificial intelligence and evolutionary algorithms for optimising cow supplementation and milk production

Efficient allocation of grain-based concentrate is essential for maximising milk yield and improving profitability in dairy farming. This study optimised concentrate allocation for dairy cows by integrating...

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