[HTML][HTML] Model averaging for identification of geochemical anomalies linked to mineralization
The complexity of geochemical patterns in the surficial media makes it necessary to consider
the uncertainty in the process of identifying geochemical anomalies. The existence of …
the uncertainty in the process of identifying geochemical anomalies. The existence of …
Digital map** for soil texture class prediction in northwestern Türkiye by different machine learning algorithms
Soil texture classes (STCs) influence the physical, chemical and biological properties of the
soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling …
soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling …
Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions
Accurate estimation of disease severity in the field is a key to minimize the yield losses in
agriculture. Existing disease severity assessment methods have poor accuracy under field …
agriculture. Existing disease severity assessment methods have poor accuracy under field …
Transferability of covariates to predict soil organic carbon in cropland soils
Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one
requirement to design and execute effective climate and food policies. In digital soil map** …
requirement to design and execute effective climate and food policies. In digital soil map** …
Digital map** of soil organic carbon with machine learning in dryland of Northeast and North plain China
Due to the importance of soil organic carbon (SOC) in supporting ecosystem services,
accurate SOC assessment is vital for scientific research and decision making. However …
accurate SOC assessment is vital for scientific research and decision making. However …
Intelligent agricultural modelling of soil nutrients and pH classification using ensemble deep learning techniques
Soil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an
efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the …
efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the …
Map** clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran
Understanding the abundance variability of clay minerals, as fundamental soil components,
will help the users to improve land management and address concerns over climate change …
will help the users to improve land management and address concerns over climate change …
Incorporating forest canopy openness and environmental covariates in predicting soil organic carbon in oak forest
The historical conversion of forests to rainfed agricultural lands in the semi-arid forest
ecosystems is one of the primary sources of human-induced, greenhouse gas emission and …
ecosystems is one of the primary sources of human-induced, greenhouse gas emission and …
Proximal and remote sensor data fusion for 3D imaging of infertile and acidic soil
Soil cation exchange capacity (CEC) and pH affect the condition of soil. To improve soil
capability in sugarcane growing areas, Sugar Research Australia introduced the Six-Easy …
capability in sugarcane growing areas, Sugar Research Australia introduced the Six-Easy …
Machine learning models for prediction of soil properties in the riparian forests
Spatial variability of soil properties is a critical factor for the planning, management, and
exploitation of soil resources. Thus, the use of different digital soil map** models to …
exploitation of soil resources. Thus, the use of different digital soil map** models to …