Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.
Abukhalaf, Nawaf and Salman, Mazen
"Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease,"
Palestine Technical University Research Journal: Vol. 2:
1, Article 1.
Available at: https://digitalcommons.aaru.edu.jo/ptuk/vol2/iss1/1