Conference Agenda

Multi-temporal remote sensing is crucial for crop monitoring, but existing mapping methods struggle with incomplete time series due to data missingness. Current models often assume consistent data, leading to performance degradation when faced with irregular or missing observations. To address this, we propose an enhanced approach combining random temporal masking with neural Ordinary Differential Equation (ODE) optimization, designed to be embedded into existing models.

Our method first employs a random temporal masking strategy during training, forcing the model to learn effective temporal dependencies from sparse, incomplete sequences, thereby boosting its adaptability to diverse missing data scenarios. Second, a time-smoothing regularization term, based on neural ODE, guides the model to learn a continuous, smooth feature trajectory from discrete observations, effectively mitigating temporal inconsistencies and abrupt fluctuations caused by missing data. We also incorporate sine-cosine positional encoding with slight perturbations for precise time representation.

We integrated our approach into the state-of-the-art TSViT model and evaluated it on the PASTIS dataset. Experiments show that while the original TSViT’s accuracy (OA and mIoU) sharply declines with increasing missing frames, our enhanced model maintains significantly better performance. At 80% missing data, our method improves OA by approximately 8% and mIoU by about 12% compared to the baseline. Qualitative results further demonstrate our model’s ability to preserve coherent, smooth spatiotemporal predictions, enhancing robustness and generalization in real-world applications.

Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced—in particular in the case of few-shot learning—failing to reflect real-world conditions. This mismatch induces a shift between training and test label distributions, degrading real-world generalization. To address this, we propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training. Specifically, we model the real-world distribution as Dirichlet-distributed random variables, effectively performing a prior augmentation during few-shot learning. Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer.

Accurate discrimination of cereal crops in heterogeneous agroecosystems requires methods that integrate both spectral and temporal information. This study proposes a compact spectral–temporal framework that combines Optimal Hyperspectral Narrowbands (OHNB) selected from EnMAP imagery using a Spectral Attention Module (SAM) with a Dynamic Time Warping (DTW)-derived phenological distance computed from Sentinel-2 EVI time series. The analysis was conducted in the Saïss region of Morocco, one of the country’s major cereal-producing areas.

SAM identified 29 physiologically meaningful narrowbands spanning the visible, red-edge, near-infrared, and shortwave-infrared regions (429–2438 nm), capturing key pigment, structural, and moisture-related vegetation properties. EVI time series were preprocessed through 10-day median compositing, linear interpolation, and Savitzky–Golay smoothing to generate stable phenological profiles. DTW quantified the temporal similarity of each field’s EVI trajectory to a cereal reference curve, producing a phenology-driven distance feature.

Three classifiers—Random Forest, SVM, and TabPFN—were evaluated under a nested standard and spatial cross-validation strategy. Using only hyperspectral bands, SVM and TabPFN achieved the highest accuracies (ROC-AUC = 0.95–0.93). Incorporating the DTW feature consistently improved performance under spatial CV, especially for RF (ROC-AUC increase: 0.89→0.91), and reduced the performance gap between validation schemes.

Overall, the fusion of SAM-selected hyperspectral bands with DTW-based phenological information enhanced spatial robustness and improved discrimination between cereal and non-cereal fields. The proposed approach offers an efficient and transferable solution for operational crop mapping in semi-arid agricultural landscapes.

Mapping banana crops in heterogeneous tropical landscapes remains challenging due to spectral similarity with surrounding vegetation, fragmented smallholder systems, and complex land-use mosaics. This study applies a deep learning approach, using a U-Net model, on high spatial resolution CBERS-4A imagery to map banana crops in Brazil’s Ribeira Valley, a subtropical region with high rainfall and heterogeneous land cover. Reference data were created through manual interpretation of satellite imagery supported by field knowledge. Representative image tiles were selected and divided into smaller patches for model training, validation, and testing. The U-Net model was trained with standard optimization techniques and evaluated using common semantic segmentation metrics. On the validation set, it achieved strong performance (accuracy 0.91, F1-score 0.84, AUC-ROC 0.96, AUC-PR 0.92). Performance was maintained or improved on the independent test set (accuracy 0.91, F1-score 0.86, AUC-ROC 0.97, AUC-PR 0.93), indicating good generalization. with high agreement between predicted and reference data. Most errors occurred at boundaries between crops and natural vegetation. Additional validation using official agricultural statistics confirmed consistency at the municipal scale. The approach demonstrates that high-resolution imagery combined with deep learning can effectively map banana crops in the region and offers a promising tool for agricultural monitoring and land-use planning in complex environments. The code, trained models, and data are publicly available at https://github.com/hnbendini/banana-unet-mapping.

This study is based on the best crop classification result generated by the proposed unsupervised Machine Learning (ML) method in Li et al., 2025a, using the spectral indices calculated by the formula with spectral bands from Sentinel-2 image products, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI) and Normalized Difference Moisture Index (NDMI). The patterns and characteristics of these spectral indices, across arable fields with different crop types following the winter growing seasons, have not yet been analyzed in detail. This research aims to provide a comprehensive study of each input spectral index and its impact on the crop classification model. Each spectral index is analyzed across a series of crop fields, using Sentinel-2 images, carefully selected to follow the patterns of winter crop phenology, and the results of unsupervised classification for each crop type in Norfolk, UK are successfully generated and analyzed. The different growing rates between winter barley and wheat have been classified found on a monthly basis using Sentinel-2 RGB images and thus the images during the harvest time, May and June, can support crop classifications. Wild grasses or other plants on the fields led to some crop misclassification from November to March in the Sentinel-2 RGB images. Similarity between winter barley and wheat and the different sowing time among the same type of crop also led to misclassification. In future these misclassifications could be avoided through better understanding of the relation between spectral indices and crop planting cycles.

Current pest monitoring relies on labor-intensive manual scouting, often leading to preventive insecticide use, highlighting the need for automated surveillance. This study presents low-cost RGB camera sensors integrated with edge artificial intelligence (AI) for real-time aphid detection, enabling timely and targeted interventions. Using field images, we trained the YOLO11-n model and evaluated its performance under commercial farming conditions, achieving an average precision of 85 % for apterous aphids. The complex structure of lettuce, with overlapping leaves and shaded areas, limits detection accuracy, particularly for nymphal stages. Nevertheless, these results pave the way for affordable precision agriculture solutions to sustainably improve pest management.