Integration of innovative technologies in the agri-food sector: the fundamentals and practical case of DNA-based traceability of olives from fruit to oil
Several socio-economic problems have been hidden by the COVID-19 pandemic crisis.
Particularly, the agricultural and food industrial sectors have been harshly affected by this …
Particularly, the agricultural and food industrial sectors have been harshly affected by this …
An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
Evaluation of river water quality index using remote sensing and artificial intelligence models
M Najafzadeh, S Basirian - Remote Sensing, 2023 - mdpi.com
To restrict the entry of polluting components into water bodies, particularly rivers, it is critical
to undertake timely monitoring and make rapid choices. Traditional techniques of assessing …
to undertake timely monitoring and make rapid choices. Traditional techniques of assessing …
Distinct behavior of biochar modulating biogeochemistry of salt-affected and acidic soil: a review
Soil degradation refers to the decline in the productive capacity of the land in a region.
Various factors responsible for soil degradation, viz., soil erosion, waterlogging, acidification …
Various factors responsible for soil degradation, viz., soil erosion, waterlogging, acidification …
Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity
Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water
moves through the soil. On the other hand, its measurement is difficult, time-consuming, and …
moves through the soil. On the other hand, its measurement is difficult, time-consuming, and …
Pre-and post-dam river water temperature alteration prediction using advanced machine learning models
Dams significantly impact river hydrology by changing the timing, size, and frequency of low
and high flows, resulting in a hydrologic regime that differs significantly from the natural flow …
and high flows, resulting in a hydrologic regime that differs significantly from the natural flow …
Application of innovative machine learning techniques for long-term rainfall prediction
Rainfall forecasting is critical because it is the componen t that has the strongest link to
natural disasters such as landslides, floods, mass movements, and avalanches. The present …
natural disasters such as landslides, floods, mass movements, and avalanches. The present …
Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms
The present research work focused on predicting the electrical conductivity (EC) of surface
water in the Upper Ganga basin using four machine learning algorithms: multilayer …
water in the Upper Ganga basin using four machine learning algorithms: multilayer …
Modelling of soil permeability using different data driven algorithms based on physical properties of soil
Soil permeability is an important parameter for assessment of infiltration, runoff, ground
water, drainage and structures design. In the current research, five different data driven …
water, drainage and structures design. In the current research, five different data driven …
[HTML][HTML] An IPSO-BP neural network for estimating wheat yield using two remotely sensed variables in the Guanzhong Plain, PR China
H Tian, P Wang, K Tansey, S Zhang, J Zhang… - … and Electronics in …, 2020 - Elsevier
Early and accurate information of crop growth condition is vital for agricultural industry and
food security, which gives rise to a strong demand for timely monitoring crop growth …
food security, which gives rise to a strong demand for timely monitoring crop growth …