LiveZilla Live Chat Software
Warning STRICT ERROR REPORTING IS ON
An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement

An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement

Starting at: Rs.5,500.00

5500 reward points

 An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement

complex impacts of disease stages and disease symptoms on spectral characteristics of the plants lead to limi-tation in disease severity detection using the spectral vegetation indices (SVIs). Although machine learning techniques have been utilized for vegetation parameters estimation and disease detec-tion, the effects of disease symptoms on their performances have been less considered. Hence, this paper investigated on 1) using partial least square regression (PLSR), ? support vector regres-sion (? -SVR), and Gaussian process regression (GPR) methods for wheat leaf rust disease detection, 2) evaluating the impact of train-ing sample size on the results, 3) the influence of disease symptoms effects on the predictions performances of the above-mentioned methods, and 4) comparisons between the performances of SVIs and machine learning techniques. In this study, the spectra of the infected and non infected leaves in different disease symptoms were measured using a non imaging spectroradiometer in the electro-magnetic region of 350 to 2500 nm. In order to produce a ground truth dataset, we employed photos of a digital camera to compute the disease severity and disease symptoms fractions. Then, differ-ent sample sizes of collected datasets were utilized to train each method. PLSR showed coefficient of determination (R2)valuesof 0.98 (root mean square error (RMSE) = 0.6) and 0.92 (RMSE=0.11) at leaf and canopy, respectively. SVR showed R2 and RMSE close to PLSR at leaf (R2= 0.98, RMSE = 0.05) and canopy (R2= 0.95, RMSE= 0.12) cales. GPR showed R2values of 0.98(RMSE = 0.03) and 0.97 (RMSE = 0.11) at leaf and canopy scale,respectively. Moreover, GPR represents better performances thanothers using small training sample size. The results represent thatthe machine learning techniques in contrast to SVIs are not sensi-tive to different disease symptoms and their results are reliable.


 


ClickMyProject Specifications
 
 
Including Packages
 
Specialization
 
  * Supporting Softwares   * 24/7 Support
  * Complete Source Code   * Ticketing System
  * Complete Documentation   * Voice Conference
  * Complete Presentation Slides   * Video On Demand *
  * Flow Diagram   * Remote Connectivity *
  * Database File   * Code Customization **
  * Screenshots   * Document Customization **
  * Execution Procedure   * Live Chat Support
  * Readme File   * Toll Free Support *
  * Addons    
  * Video Tutorials    
       
 

*- PremiumSupport Service (Based on Service Hours) ** - Premium Development Service (Based on Requirements)


Add to Cart:

  • Model: PROJ7516
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

Please Choose:

Downloadable







This product was added to our catalog on Saturday 29 July, 2017.

  0