Machine learning applications for precision agriculture: A comprehensive review
Agriculture plays a vital role in the economic growth of any country. With the increase of
population, frequent changes in climatic conditions and limited resources, it becomes a …
population, frequent changes in climatic conditions and limited resources, it becomes a …
Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges
M Torky, AE Hassanein - Computers and Electronics in Agriculture, 2020 - Elsevier
Blockchain quickly became an important technology in many applications of precision
agriculture discipline. The need to develop smart P2P systems capable of verifying …
agriculture discipline. The need to develop smart P2P systems capable of verifying …
Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine
B Feizizadeh, D Omarzadeh… - Journal of …, 2023 - Taylor & Francis
With the recent advances in earth observation technologies, the increasing availability of
data from more and more different satellite sensors as well as progress in semi-automated …
data from more and more different satellite sensors as well as progress in semi-automated …
Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data
CONTEXT Mapping crop types from satellite images is a promising application in
agricultural systems. However, it is a challenge to automate in-season crop type mapping …
agricultural systems. However, it is a challenge to automate in-season crop type mapping …
Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm
Abstract Space-based crop identification and acreage estimation have played a significant
role in agricultural studies in recent years, due to the development of Remote Sensing …
role in agricultural studies in recent years, due to the development of Remote Sensing …
[HTML][HTML] Mapping corn dynamics using limited but representative samples with adaptive strategies
Mapping the corn dynamics at a large scale and multiple years is essential for global food
security. Traditional mapping approaches by collecting training samples from field surveys …
security. Traditional mapping approaches by collecting training samples from field surveys …
A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering
Yield prediction is essential in food security, food trade, and field management. However,
due to the associated complex formation mechanisms of yield, accurate and timely yield …
due to the associated complex formation mechanisms of yield, accurate and timely yield …
[HTML][HTML] A predictive analytics model for crop suitability and productivity with tree-based ensemble learning
The increasing global population has heightened the importance of agriculture and the need
for food security. This study focuses on developing and evaluating tree-based ensemble …
for food security. This study focuses on developing and evaluating tree-based ensemble …
Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples
Training samples is fundamental for crop mapping from remotely sensed images, but difficult
to acquire in many regions through ground survey, causing significant challenge for crop …
to acquire in many regions through ground survey, causing significant challenge for crop …
Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer
A timely and detailed crop-specific land cover map can support many agricultural
applications and decision makings. However, in-season crop mapping over a large area is …
applications and decision makings. However, in-season crop mapping over a large area is …