A systematic literature review on crop yield prediction with deep learning and remote sensing
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 …
to automatically extract features and learn from the datasets. Meanwhile, smart farming …
High-resolution satellite imagery applications in crop phenotyping: An overview
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 …
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
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 …
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 …
agribusiness. This paper provides a novel demonstration of the use of freely available …
Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning
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 …
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
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 …
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
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 …
essential food for the world population. Because the United States is a major producer and …
Extreme weather events in agriculture: A systematic review
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 …
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
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 …
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
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 …
precision agriculture applications. Leaf Area Index (LAI) is an important parameter that …