A5032177589- Greenhouse Technology and Climate Control
- Advanced Data Processing Techniques
- Neural Networks and Applications
- Digital Transformation in Industry
- Climate change impacts on agriculture
- Smart Agriculture and AI
- Soil and Water Nutrient Dynamics
- Hydrological Forecasting Using AI
- Big Data Technologies and Applications
- Data Management and Algorithms
- Remote Sensing in Agriculture
- Pasture and Agricultural Systems
- Species Distribution and Climate Change
- Hydrology and Watershed Management Studies
- Simulation Techniques and Applications
- IoT and Edge/Fog Computing
- Irrigation Practices and Water Management
- Air Quality Monitoring and Forecasting
- Soil Carbon and Nitrogen Dynamics
- Plant Water Relations and Carbon Dynamics
Wageningen University & Research
2020-2024
Digital twins are being adopted by increasingly more industries, transforming them and bringing new opportunities. provide previously unheard levels of control over physical entities help to manage complex systems integrating an array technologies. Recently, agriculture has seen several technological advancements, but it is still unclear if this community making effort adopt digital in its operations. In work, we employ a mixed-method approach investigate the added-value for agriculture. We...
Many studies have applied machine learning to crop yield prediction with a focus on specific case studies. The data and methods they used may not be transferable other crops locations. On the hand, operational large-scale systems, such as European Commission's MARS Crop Yield Forecasting System (MCYFS), do use learning. Machine is promising method especially when large amounts of are being collected published. We combined agronomic principles modeling build baseline for forecasting. workflow...
In the environmental sciences, there are ongoing efforts to combine multiple models assist analysis of complex systems. Combining process-based models, which have encoded domain knowledge, with machine learning can flexibly adapt input data, improve modeling capabilities. However, both types data limitations. We propose a methodology overcome these issues by using model generate aggregating them lower resolution mimic real situations, and developing fraction inputs. showcase this method case...
Abstract Domain adaptation is important in agriculture because agricultural systems have their own individual characteristics. Applying the same treatment practices (e.g., fertilization) to different may not desired effect due those also an inherent aspect of digital twins. In this work, we examine potential transfer learning for domain pasture We use a synthetic dataset grassland simulations pretrain and fine-tune machine metamodels nitrogen response rate prediction. investigate outcome...
Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult calibrate for local conditions, or data-driven machine learning methods, are known require large datasets. In this work we investigate potato using a hybrid meta-modeling approach. A model is employed generate synthetic data (pre)training convolutional neural net, then fine-tuned with observational data. When applied in silico, our approach...
Learning latent representations has aided operational decision-making in several disciplines. Its advantages include uncovering hidden interactions data and automating procedures which were performed manually the past. Representation learning is also being adopted by earth environmental sciences. However, there are still subfields that depend on manual feature engineering based expert knowledge use of algorithms do not utilize space. Relying those techniques can inhibit since they impose...
<p>In this work we compare the performance of machine learning metamodels different scale for prediction pasture grass nitrogen response rate using a case study across locations in New Zealand. We first used range soil, plant and management parameters known to affect growth and/or response. These generated complete factorial that enabled us run virtual experiments, APSIM simulation model, eight around country. included 40 years weather data capture effect variability on rate....