Asset Details
Do you wish to reserve the book?
Application of soft computing methods and spectral reflectance data for wheat growth monitoring
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Application of soft computing methods and spectral reflectance data for wheat growth monitoring
Do you wish to request the book?
Application of soft computing methods and spectral reflectance data for wheat growth monitoring
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Application of soft computing methods and spectral reflectance data for wheat growth monitoring
2019
Overview
Technology of precision agriculture has caused to the remote sensors development that
compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices
obtained from remote sensing data can help to summarize climate conditions. Artificial
Neural Networks (ANNs), as a soft computing methods, are one of the most efficient methods
for computing as compared to the statistical and analytical techniques for spectral data. This
study was employed experimental radial basis function (RBF) of ANN models and adaptive
neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant
analysis development (SPAD), protein content and grain yield of wheat plant based on
spectral reflectance value and to compare two models. Results indicated that the obtained
results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in
2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low
RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407,
105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS
models.
Publisher
University of Baghdad, College of Agriculture,Baghdad University
We currently cannot retrieve any items related to this title. Kindly check back at a later time.