A systematic literature review on crop yield prediction with deep learning and remote sensing

P Muruganantham, S Wibowo, S Grandhi, NH Samrat… - Remote Sensing, 2022 - mdpi.com
Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

High-resolution satellite imagery applications in crop phenotyping: An overview

C Zhang, A Marzougui, S Sankaran - Computers and Electronics in …, 2020 - Elsevier
Over the past ten years, plant phenotyping technologies that utilize sensing and data mining
approaches to estimate crop traits in a high-throughput and objective manner, have been …

Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean

M Yoosefzadeh-Najafabadi, HJ Earl, D Tulpan… - Frontiers in plant …, 2021 - frontiersin.org
Recent substantial advances in high-throughput field phenotyping have provided plant
breeders with affordable and efficient tools for evaluating a large number of genotypes for …

High resolution wheat yield mapping using Sentinel-2

ML Hunt, GA Blackburn, L Carrasco… - Remote Sensing of …, 2019 - Elsevier
Accurate crop yield estimates are important for governments, farmers, scientists and
agribusiness. This paper provides a novel demonstration of the use of freely available …

Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning

L Feng, Z Zhang, Y Ma, Q Du, P Williams, J Drewry… - Remote Sensing, 2020 - mdpi.com
Alfalfa is a valuable and intensively produced forage crop in the United States, and the
timely estimation of its yield can inform precision management decisions. However …

Monitoring within-field variability of corn yield using Sentinel-2 and machine learning techniques

A Kayad, M Sozzi, S Gatto, F Marinello, F Pirotti - Remote Sensing, 2019 - mdpi.com
Monitoring and prediction of within-field crop variability can support farmers to make the right
decisions in different situations. The current advances in remote sensing and the availability …

Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States

Y Wang, Z Zhang, L Feng, Q Du, T Runge - Remote Sensing, 2020 - mdpi.com
Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying
essential food for the world population. Because the United States is a major producer and …

Extreme weather events in agriculture: A systematic review

A Cogato, F Meggio, M De Antoni Migliorati… - Sustainability, 2019 - mdpi.com
Despite the increase of publications focusing on the consequences of extreme weather
events (EWE) for the agricultural sector, a specific review of EWE related to agriculture is …

Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy

A Kayad, M Sozzi, S Gatto, B Whelan, L Sartori… - … and Electronics in …, 2021 - Elsevier
Farmer's management decisions and environmental factors are the main drivers for field
spatial and temporal yield variability. In this study, a 22 ha field cultivated with corn for more …

[HTML][HTML] Radiative transfer model inversion using high-resolution hyperspectral airborne imagery–Retrieving maize LAI to access biomass and grain yield

A Kayad, FA Rodrigues Jr, S Naranjo, M Sozzi… - Field Crops …, 2022 - Elsevier
Mapping crop within-field yield variability provide an essential piece of information for
precision agriculture applications. Leaf Area Index (LAI) is an important parameter that …