Photon sensor-based monitoring of spatial variations in canopy FIPAR for crop growth assessment

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100771 - 100771

Опубликована: Янв. 1, 2025

Язык: Английский

---

Near‐surface permafrost extent and active layer thickness characterized by reanalysis/assimilation data

Atmospheric Science Letters, Год журнала: 2025, Номер 26(1)

Опубликована: Янв. 1, 2025

Abstract Whilst permafrost change is widely concerned in the context of global warming, lack observations becomes one major limitations for conducting large‐scale and long‐term research. Reanalysis/assimilation data theory can make up observations, but how they characterize extent active layer thickness remains unclear. Here, we investigate near‐surface characterized by seven reanalysis/assimilation datasets (CFSR, MERRA‐2, ERA5, ERA5‐Land, GLDAS‐CLSMv20, GLDAS‐CLSMv21, GLDAS‐Noah). Results indicate that most have limited abilities characterizing thickness. GLDAS‐CLSMv20 overall optimal terms comprehensive performance both present‐day change. The indicates decreases −0.69 × 10 6 km 2 decade −1 deepens 0.06 m from 1979 to 2014. Change significantly correlated air temperature, precipitation, downward longwave radiation summer, correlations show regional differences. Our study implies an imperative advance data's reproduce permafrost, especially reanalysis data.

Язык: Английский

---

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Agronomy, Год журнала: 2024, Номер 14(11), С. 2605 - 2605

Опубликована: Ноя. 4, 2024

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction large dataset requirements. overcomes these by using multi-scale weighted aggregation interaction module, enhancing both global extraction. The method refines training process, boosting accuracy robustness. With 8226 images, achieved classification 96.58%, an F1 score 96.10%, Matthews Correlation Coefficient (MCC) 92.24%, outperforming other models. inclusion Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced capture. Ablation studies revealed contributed to 0.56% improvement 0.34% increase in MCC. In summary, presents innovative solution disease diagnosis, potential applications broader agricultural detection, ultimately aiding precision agriculture health management.

Язык: Английский

---

Investigation of coupling DSSAT with SCOPE-RTMo via sensitivity analysis and use of this coupled crop-radiative transfer model for sensitivity-based data assimilation

European Journal of Agronomy, Год журнала: 2024, Номер 162, С. 127431 - 127431

Опубликована: Ноя. 15, 2024

Язык: Английский

---