Machine learning in agriculture: A comprehensive updated review

L Benos, AC Tagarakis, G Dolias, R Berruto, D Kateris… - Sensors, 2021‏ - mdpi.com
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

Deep learning in environmental remote sensing: Achievements and challenges

Q Yuan, H Shen, T Li, Z Li, S Li, Y Jiang, H Xu… - Remote sensing of …, 2020‏ - Elsevier
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

[HTML][HTML] Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover map**: A systematic review

M ED Chaves, M CA Picoli, I D. Sanches - Remote Sensing, 2020‏ - mdpi.com
Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2
MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land …

Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches

J Cao, Z Zhang, F Tao, L Zhang, Y Luo, J Zhang… - Agricultural and Forest …, 2021‏ - Elsevier
Timely and reliable yield prediction at a large scale is imperative and prerequisite to prevent
climate risk and ensure food security, especially with climate change and increasing …

A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020

S He, Y Zhang, N Ma, J Tian, D Kong… - Earth System Science …, 2022‏ - essd.copernicus.org
Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP)
information is essential for understanding the large-scale water and carbon dynamics …

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods

E Kamir, F Waldner, Z Hochman - ISPRS Journal of Photogrammetry and …, 2020‏ - Elsevier
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 …

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

D Tuia, K Schindler, B Demir, XX Zhu… - … and Remote Sensing …, 2024‏ - ieeexplore.ieee.org
Earth observation (EO) is increasingly used for map** and monitoring processes
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …

Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling

Y Zhao, AB Potgieter, M Zhang, B Wu, GL Hammer - Remote Sensing, 2020‏ - mdpi.com
Accurate prediction of crop yield at the field scale is critical to addressing crop production
challenges and reducing the impacts of climate variability and change. Recently released …

[HTML][HTML] Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques

LP Osco, JM Junior, APM Ramos, DEG Furuya… - Remote Sensing, 2020‏ - mdpi.com
Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential,
reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis …

[HTML][HTML] Map** winter crops using a phenology algorithm, time-series Sentinel-2 and Landsat-7/8 images, and Google Earth Engine

L Pan, H **a, X Zhao, Y Guo, Y Qin - Remote sensing, 2021‏ - mdpi.com
With the increasing population and continuation of climate change, an adequate food supply
is vital to economic development and social stability. Winter crops are important crop types …