TY - GEN A1 - Ryo, Masahiro A1 - Schiller, Josepha A1 - Stiller, Stefan A1 - Palacio, Juan Camilo Rivera A1 - Mengsuwan, Konlavach A1 - Safonova, Anastasiia A1 - Wei, Yuqi T1 - Deep learning for sustainable agriculture needs ecology and human involvement T2 - Journal of Sustainable Agriculture and Environment N2 - Deep learning is an emerging data analytic tool that can improve predictability, efficiency and sustainability in agriculture. With a bibliometric analysis of 156 articles, we show how deep learning methods have been applied in the context of sustainable agriculture. As a general publication trend, China and India are leading countries for publication, international collaboration is still minor. Deep learning has been popularly applied in the context of smart agriculture across scales for individual plant monitoring, field monitoring, field operation and robotics, predicting soil, water and climate conditions and landscape‐level monitoring of land use and crop types. We identified that the potential of deep learning had been investigated mainly for predicting soil (abiotic), water, climate and vegetation dynamics, but ecological characteristics are critically understudied. We also highlight key themes that can be better addressed with deep learning for fostering sustainable agriculture: (i) including above‐ and belowground ecological dynamics such as ecosystem functioning and ecotone, (ii) evaluating agricultural impacts on other ecosystems and (iii) incorporating the knowledge and opinions of domain experts and stakeholders into artificial intelligence. We propose that deep learning needs to go beyond automatic data analysis by integrating ecological and human knowledge to foster sustainable agriculture. KW - biodiversity KW - deep learning KW - ecology KW - smart agriculture KW - sustainable agriculture Y1 - 2022 U6 - https://doi.org/10.1002/sae2.12036 SN - 2767-035X VL - 2 IS - 1 SP - 40 EP - 44 PB - Wiley ER - TY - GEN A1 - Safonova, Anastasiia A1 - Ghazaryan, Gohar A1 - Stiller, Stefan A1 - Main-Knorn, Magdalena A1 - Nendel, Claas A1 - Ryo, Masahiro T1 - Ten deep learning techniques to address small data problems with remote sensing T2 - International Journal of Applied Earth Observation and Geoinformation N2 - Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability. We then introduce an overview of ten promising DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. Our particular contribution was to develop a flowchart that helps DL users select which technique to use given by answering a few questions. We hope that our review article facilitate DL applications to tackle societally important environmental problems with limited reference data. KW - Small data problems KW - Remote sensing KW - Deep learning KW - Transfer learning KW - Few-shot learning KW - Zero-shot learning KW - Self-supervised learning Y1 - 2023 U6 - https://doi.org/10.1016/j.jag.2023.103569 SN - 1569-8432 VL - 125 PB - Elsevier BV ER - TY - GEN A1 - Safonova, Anastasiia A1 - Ryo, Masahiro T1 - Deep learning improves point density in PS-InSAR data toward finer-scale land surface displacement detection T2 - IEEE Access N2 - The permanent scatterer interferometric aperture radar (PS-InSAR) technique is used to measure and monitor displacements of the Earth’s surface over time. While the approach is promising for large-scale deformation, the density of the received PS points is insufficient for localized deformation analysis. In this first work, we aim to improve the technique by increasing the point density of high-precision deformation monitoring in PS-InSAR data by developing a convolutional long short-term memory (ConvLSTM) model that predicts PS points on different land covers, such as forest, urban, natural, water, and combinations among them. The proposed architecture, PS-ConvLSTM, was trained on a temporary dataset with interferograms to classify stable and unstable PS pixels from over 200,000 site images obtained from the city of Barcelona, Spain. The result showed that the trained PS-ConvLSTM model is highly compatible with the method currently used, which requires a large manual effort by an expert (accuracy: 99%). In addition, the proposed approach increased the point density by 15%, indicating that ConvLSTM is a promising approach for increasing the point density in PS-InSAR data and thus improving localized deformation analysis. KW - Deep learning KW - recurrent neural network KW - long short-term memory KW - point density KW - persistent scatter KW - InSAR process Y1 - 2024 U6 - https://doi.org/10.1109/ACCESS.2024.3459099 SN - 2169-3536 VL - 12 SP - 132754 EP - 132762 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Safonova, Anastasiia A1 - Stiller, Stefan A1 - Yordanov, Momchil A1 - Ryo, Masahiro T1 - Self-supervised learning outperforms supervised learning for crop classification by annotating only 5% of images T2 - Precision agriculture N2 - Purpose One of the most pervasive Artificial Intelligence (AI) methodologies utilized in the domain of agriculture for image-based classification purposes is Supervised Learning (SL). However, SL depends on a large amount of annotation effort and is susceptible to overfitting to the given prediction task. Self-Supervised Learning (SSL) is a novel training paradigm with the potential to address these issues, while its potential has not been investigated in the agriculture domain. This paper presents the initial experimental investigation and comparison of SL and SSL for the classification of agricultural images in the context of limited samples. Methods We used an agricultural subset of the Land Use and Cover Area Frame Survey (LUCAS) dataset serving as a case study. In total, it comprised 1,000 images for each of the 10 crops: common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape and turnip rape, soya, and temporary grassland. For SL, we trained popular and frequently used Convolutional Neural Network (CNN) architectures such as VGG16, Inception, ResNet-18/50, SqueezeNet, ResNeXt-50, MobileNet-V2, ShuffleNet, EfficientNet-V2, and ConvNeXt Tiny with and without data augmentations. For SSL, the best-performing CNN architectures (ResNet-18, ResNet-50, and ResNeXt-50) were further tested. The architectures were pre-trained with the VICReg algorithm (Variance Invariance Covariance Regularization) and fine-tuned successively using supervision for crop type classification. Results Our results demonstrate that the SSL models can distinguish crop types (common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape, soya, and grassland) even without labels based solely on morphological features and organize them into three semantically meaningful visual groups: cereal-like and grassland crops, upright broadleaf crops, and low-growing broadleaf crops. The fine-tuned models, particularly ResNeXt-50, achieved superior performance compared to any of the SLs. Notably, we show that the fine-tuned SSL models outperformed the best-performing SL models by using only 5% of the labeled training data for fine-tuning, corresponding to a small and balanced subset of the training split. Conclusion These findings highlight the potential of SSL for improving classification efficiency and generalization under limited data availability conditions in agriculture applications, providing a viable path toward more efficient agricultural monitoring systems. KW - Supervised learning KW - Self-supervised learning KW - VICReg KW - LUCAS dataset KW - Image classification KW - Small data problem KW - Agricultural crop Y1 - 2025 U6 - https://doi.org/10.1007/s11119-025-10302-9 SN - 1385-2256 VL - 27 IS - 1 SP - 1 EP - 31 PB - Springer Science and Business Media LLC CY - Dordrecht ER -