Results 31 to 40 of about 3,154,862 (212)

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

Crop Yield Prediction Using Epsilon Density Based Prediction

, 2021
Machine learning algorithms play a significant role in data analysis in many disciplines likeAgriculture, Food, Medicine, and Twitter Data. Yield prediction is a significant agricultural problem thatremains to be solved based on the available data ...
D. Esther Rani, Dr.N.Sathyanarayana, Dr. B. Vishnu Vardhan
core   +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

Crop yield prediction using deep learning

, 2022
Multi- Model Ensemble with Deep Neural Network Based Crop Yield ...
M.Saranya Dr (14247557)
core   +1 more source

Genetic relationships between spring emergence, canopy phenology, and biomass yield increase the accuracy of genomic prediction in Miscanthus [PDF]

, 2017
Miscanthus has potential as a bioenergy crop but the rapid development of high-yielding varieties is challenging. Previous studies have suggested that phenology and canopy height are important determinants of biomass yield. Furthermore, while genome-wide
Robson, P., Sarah Hawkins, Kerrie Farrar, Davey, C. L., John C Clifton-Brown, Slavov, G., Clifton-Brown, J. C., Hawkins, S., Farrar, K., Paul Robson, Gancho T Slavov, Donnison, I. S., Iain S Donnison, Christopher L Davey, Slavov, Gancho   +14 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