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| 1 | Spa- tial variability in crop response under contour hedgerow systems in the Andes region of Ecuador 显示文摘 | GERD DERCON JOZEF DECKERS JEAN POESEN | 2006 | Soil and Tillage Research2006，86，： | 1 |
| 2 | Integration of mid-infrared spectroscopy and geostatistics in the assess- ment of soil spatial variability at landscape level显示文摘 | JUAN Guillermo Cobo GERD Dercon TSITSI Yekeye | 2010 | Geoderma2010，158，34： | 1 |
| 3 | Spatial variability in soil properties on slow-forming terraces in the Andes region of Ecuador显示文摘 | Gerd Dercon Jozef Deckers Gerard Govers | 2003 | Soil & Tillage Research2003，72，： | 1 |
| 4 | Spatial variability in soil properties on slow -forming terraces in the Andes region of Ecuador显示文摘 | Gerd Dercon Jozef Deckers Gerard C-overs | 2003 | Soil and Tillage Research2003，72，1： | 1 |
| 5 | 遥感作物制图辅助核事故农业风险决策显示文摘【目的】将作物时空分布数据应用于核事故农业风险决策支持系统,体现遥感作物制图在核事故农业风险决策中的重要性。【方法】文章以大亚湾核电基地为研究案例,对其周边地区的作物轮作系统进行遥感制图;作物时空分布数据经后处理,上传至核事故农业风险决策支持系统,实现作物样本任务的自动生成,以及放射性核素浓度的时空分布模拟。【结果】提出的遥感制图方法可以在耕地破碎、云雨繁密区识别作物轮作系统,快速、准确地提供大范围作物时空分布数据。经过处理的作物时空分布数据,能够方便地应用于决策支持系统,辅助完成特定或优先区作物样本任务点的自动生成,以及放射性核素浓度时空分布的模拟。【结论】遥感作物制图与核事故农业风险决策支持系统相结合,可进一步提高采样的有效性,提升放射性核素空间和时间分布模拟与预测的准确性。从而帮助决策者制定核污染监测和评估策略、修复计划,科学指导农业生产的恢复。未来,有必要深入研究遥感作物制图在核事故农业风险决策中的应用,充分发挥遥感技术与数据的优势,规避核事故对农业生产带来的风险。 | 刘园 Lazar Adjigogov Franck Albinet Gerd Dercon 余强毅 吴文斌 周清波 | 2022 | 中国农业信息2022，34，1： | 0 |
| 6 | Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning:From prediction to explainability显示文摘The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders. | Franck Albinet Yi Peng Tetsuya Eguchi Erik Smolders Gerd Dercon | 2022 | Artificial Intelligence in Agriculture2022，，1： | 0 |
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