Results 41 to 50 of about 3,276,216 (308)

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

Agronomy, 2019
An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the ...
Esther Grüner, Thomas Astor, Michael Wachendorf   +2 more
doaj   +1 more source

Creeping Bentgrass Yield Prediction With Machine Learning Models

Frontiers in Plant Science, 2021
Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate.
Qiyu Zhou, Douglas J. Soldat
doaj   +1 more source

Effects of canopy management practices on grapevine bud fruitfulness

OENO One, 2020
Background and aims: Bud fruitfulness is a key component of grapevine reproductive performance as it determines crop production for the following growing season.
Cassandra Collins, Xiaoyi Wang, Stephen Lesefko, Roberta De Bei, Sigfredo Fuentes   +4 more
doaj   +1 more source

Modelling the Influence of Soil Properties on Crop Yields Using a Non-Linear NFIR Model and Laboratory Data

Soil Systems, 2021
This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN,
Rebecca L. Whetton, Yifan Zhao, Said Nawar, Abdul M. Mouazen   +3 more
doaj   +1 more source

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Sensors, 2021
Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided ...
Zhonglin Ji, Yaozhong Pan, Xiufang Zhu, Jinyun Wang, Qiannan Li   +4 more
doaj   +1 more source

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables [PDF]

, 2022
Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making.
Ahmed, A. A. Masrur, Sharma, Ekta, Raj, Nawin, Md Wasique Islam Chowdhury, A. A. Masrur Ahmed, Soar, Jeffrey, S Janifer Jabin Jui, Nawin Raj, Jeffrey Soar, Aditi Bose, Bose, Aditi, Ekta Sharma, Jui, S. Janifer Jabin, Chowdhury, Md Wasique Islam   +13 more
core   +1 more source

A Review of CNN Applications in Smart Agriculture Using Multimodal Data

Sensors
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield
Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari, Josiane Mothe   +3 more
doaj   +1 more source

Modeling of the Effect of Process Variations on a Micromachined Doubly-Clamped Beam

Micromachines, 2017
In the fabrication of micro-electro-mechanical systems (MEMS) devices, manufacturing process variations are usually involved. For these devices sensitive to process variations such as doubly-clamped beams, mismatches between designs and final products ...
Lili Gao, Zai-Fa Zhou, Qing-An Huang
doaj   +1 more source

Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US

Sensors
Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers.
Sayantan Sarkar, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, Douglas R. Smith   +5 more
doaj   +1 more source

Bridging the Loneliness Gap: Depression, Connectivity, and Isolation in Pediatric Oncology Patients and Their Peers

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Loneliness is associated with adverse physical and mental health outcomes and remains understudied in children and adolescents undergoing cancer therapy. Pediatric oncology patients may be at increased risk due to medical isolation and disruption of social networks.
Charlotte N. Stahlfeld, Scott H. Maurer, Michelle D. Watkinson, John Nury, Elise Barberis, Molly C. Mack   +5 more
wiley   +1 more source