Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data
The enormous increase of remote sensing data from airborne and space-borne platforms, as
well as ground measurements has directed the attention of scientists towards new and …
well as ground measurements has directed the attention of scientists towards new and …
Seasonal crop yield forecast: Methods, applications, and accuracies
The perfect knowledge of yield before harvest has been a wish puzzling human being since
the beginning of agriculture because seasonal forecast of crop yield plays a critical role in …
the beginning of agriculture because seasonal forecast of crop yield plays a critical role in …
Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches
JP Bharadiya, NT Tzenios… - Journal of Engineering …, 2023 - classical.goforpromo.com
The art of predicting crop production is done before the crop is harvested. Crop output
forecasts will help people make timely judgments concerning food policy, prices in markets …
forecasts will help people make timely judgments concerning food policy, prices in markets …
Deep learning classification of land cover and crop types using remote sensing data
Deep learning (DL) is a powerful state-of-the-art technique for image processing including
remote sensing (RS) images. This letter describes a multilevel DL architecture that targets …
remote sensing (RS) images. This letter describes a multilevel DL architecture that targets …
Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods
Closing the yield gap between actual and potential wheat yields in Australia is important to
meet the growing global demand for food. The identification of hotspots of the yield gap …
meet the growing global demand for food. The identification of hotspots of the yield gap …
[HTML][HTML] Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping
Many applied problems arising in agricultural monitoring and food security require reliable
crop maps at national or global scale. Large scale crop mapping requires processing and …
crop maps at national or global scale. Large scale crop mapping requires processing and …
Emerging trends in machine learning to predict crop yield and study its influential factors: A survey
Agriculture is one of the most crucial field contributing to the development of any nation. It
not only affects the economy of nation but also has impact on the world food grain statistics …
not only affects the economy of nation but also has impact on the world food grain statistics …
Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique
Early and reliable seasonal crop yield forecasts are crucial for both farmers and decision-
makers. Commonly-used methods for seasonal yield forecasting are based on process …
makers. Commonly-used methods for seasonal yield forecasting are based on process …
Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices
In the present study, multiple linear regression models were constructed to simulate the yield
of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000–2016 time period …
of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000–2016 time period …
[HTML][HTML] Early-season mapping of winter crops using sentinel-2 optical imagery
H Tian, Y Wang, T Chen, L Zhang, Y Qin - Remote Sensing, 2021 - mdpi.com
Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal
resolution in addition to free access. The objective of this paper was to evaluate the potential …
resolution in addition to free access. The objective of this paper was to evaluate the potential …