Remote-sensing data and deep-learning techniques in crop map** and yield prediction: A systematic review

A Joshi, B Pradhan, S Gite, S Chakraborty - Remote Sensing, 2023‏ - mdpi.com
Reliable and timely crop-yield prediction and crop map** are crucial for food security and
decision making in the food industry and in agro-environmental management. The global …

Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals

PC Pandey, M Pandey - Sustainable Development, 2023‏ - Wiley Online Library
Food security is a global challenge that aligns with several Sustainable Development Goals
(SDGs), including SDG 1‐“No Poverty”, SDG 2‐“Zero Hunger,” SDG 3‐“Good Health and …

Multiscale 3-D–2-D mixed CNN and lightweight attention-free transformer for hyperspectral and LiDAR classification

L Sun, X Wang, Y Zheng, Z Wu… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
The effective combination of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data can be used for land cover classification. Recently, deep-learning-based …

[HTML][HTML] Evaluation of 18 satellite-and model-based soil moisture products using in situ measurements from 826 sensors

HE Beck, M Pan, DG Miralles… - Hydrology and Earth …, 2021‏ - hess.copernicus.org
Abstract Information about the spatiotemporal variability of soil moisture is critical for many
purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction …

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

PR Bhagat, F Naz, R Magda - PloS one, 2022‏ - journals.plos.org
There is a dearth of literature that provides a bibliometric analysis concerning the role of
Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this …

Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications

Z Zhou, Y Majeed, GD Naranjo… - … and Electronics in …, 2021‏ - Elsevier
With the increasing global water scarcity, efficient assessment methods for crop water stress
have become a prerequisite to perform precision irrigation scheduling. The 1accessibility of …

Multi-layer high-resolution soil moisture estimation using machine learning over the United States

L Karthikeyan, AK Mishra - Remote Sensing of Environment, 2021‏ - Elsevier
The lack of proper understanding of multi-layer soil moisture (SM) profile (signals) remains a
persistent challenge in sustainable agricultural water management and food security …

Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images

M Shu, Q Li, A Ghafoor, J Zhu, B Li, Y Ma - European Journal of Agronomy, 2023‏ - Elsevier
The rapid and efficient estimation of aboveground biomass (AGB) in maize proves
advantageous for growth assessment, grain quality and yield prediction, and timely field …

[HTML][HTML] A parallel-cascaded ensemble of machine learning models for crop type classification in Google earth engine using multi-temporal sentinel-1/2 and landsat-8 …

E Abdali, MJ Valadan Zoej, A Taheri Dehkordi… - Remote Sensing, 2023‏ - mdpi.com
The accurate map** of crop types is crucial for ensuring food security. Remote Sensing
(RS) satellite data have emerged as a promising tool in this field, offering broad spatial …

[HTML][HTML] Remote sensing monitoring of rice diseases and pests from different data sources: A review

Q Zheng, W Huang, Q **a, Y Dong, H Ye, H Jiang… - Agronomy, 2023‏ - mdpi.com
Rice is an important food crop in China, and diseases and pests are the main factors
threatening its safety, ecology, and efficient production. The development of remote sensing …