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Open access

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

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...

YOLO-GPP: End-to-end prediction of the grasp position and pose on tomato peduncle for robotic harvesting

A high fresh fruit harvesting success rate relies on the real-time and precise determination of the optimal grasping position and pose of the target fruit. This study proposes an end-to-end grasp pose...

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...

Recent advances in crop pest detection, forecasting and early warning: A review

Crop insect pests are critical biotic stressors disrupting plant physiological functions. Their infestations not only cause direct yield losses but also threaten global food security and agricultural...

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...

Can generative AI make farming decisions? Current status and future pathways – A case study in row crop production with ChatGPT

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key...

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...

Empowering Chinese medicinal agriculture through AI-driven technologies: A comprehensive review

Traditional Chinese medicine, with its rich history and profound influence on healthcare, is deeply rooted in medicinal plants, which serve as the foundation for Chinese herbal medicines (CHMs). However,...

AgriMAPO: A multimodal automatic prompt optimization approach for crop disease classification using large language models

Accurate identification of crop diseases is a core challenge in promoting precision agriculture and reducing yield losses. Current deep learning-based recognition methods heavily rely on large-scale...

AELVI-SLAM: LiDAR–visual–inertial SLAM for autonomous exploration in multiple agricultural scenarios

Accurate simultaneous localization and mapping (SLAM) algorithms are essential for enhancing the operational efficiency of agricultural robots. However, existing SLAM methods are vulnerable to dynamic...

Detecting wheat powdery mildew through the integration of two-dimensional correlation spectra and dual-mode deep learning

Accurate and timely monitoring of wheat powdery mildew (WPM) severity is essential for precision crop management and yield loss mitigation. Hyperspectral technology offers a non-destructive detection...

From plots to region: Machine learning-based UAV-satellite integration for mapping fractional coverage of peanut southern blight

Peanut southern blight (PSB), a soil-borne fungal disease, threatens peanut production. Because PSB originates at the root collar, canopy symptoms develop gradually and subtly, limiting satellite monitoring....

Design and implementation of a compact fresh tea-leaf sorting system for integrated harvesting–sorting equipment in hilly mountainous tea garden

Mechanically harvested tea leaves show poor uniformity and cannot directly meet the raw material homogeneity required for famous tea processing. This study develops a compact fresh leaf sorting system...

Combining transfer learning and hyperspectral data to identify the severity of mild Fusarium head blight infection in wheat

Accurately identifying mildly infected wheat Fusarium head blight (FHB) is crucial for early disease monitoring. However, the weak spectral signals from mildly infected wheat significantly limit the...

Seed imaging omics: A bridge from perception to cognition for the future of seed phenotyping

Seeds are complex living systems that display rich diversity in morphology, physiology, biochemistry, and genetics. Yet phenotyping during seed dormancy remains hampered by limited imaging modalities,...

Advancing the prediction of nitrogen utilization efficiency in wheat by integrating high-throughput phenotyping into the WheatGrow model

Accurate prediction of nitrogen utilization efficiency (NUtE) is critical for breeding nitrogen-efficient crop cultivars and optimizing field nitrogen management. Traditional prediction methods are...

An apple tree pruning robot system based on branch segmentation and decision-making control

Intelligent pruning is an important part of smart orchard management operations, and the study of intelligent pruning robot is of great significance for the construction of smart orchards. This paper...

Detection and variable spraying of maize and weeds in residue covered fields via occlusion-aware modeling

Straw mulching is an important practice in conservation tillage. Although it improves soil quality and moisture retention, it also creates strong occlusion between crops and weeds, which reduces the...

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,...

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...

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