abstract

• This study establishes a methodology to identify some defined powdery mildew infection levels in cucurbits plants based on the Partial Least Square Regression (PLSR) coefficients extracted from their spectral signatures. Here, the PLRS decreases the amount of data to be processed. Thus, some conditions of the leaves could be classified by using the PLSR coefficients and Support Vector Machine (SVM). For this case, different parameters in the plants and the growing season are the response concerning variables. To classify four stages of plant: i) healthy leaves, ii) leaves in a stage of germination of the fungus, iii) leaves with first symptoms and iv) diseased leaves. The powdery mildew levels were identified with an accuracy of 92%25 and a kappa value of 0.81. This analysis propose a feature extraction that could be applied to other plants analysis. © 2022 IEEE.

authors

• Morales Saldaña Jorge Alberto
• Palacios Hernández Elvia Ruth
• Reyes-Portillo I.A.

publication date

• 2022-01-01

published in

• 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022  Proceedings

keywords

• Fungi; Support vector machines; Classifieds; Condition; Growing season; Partial least square regression; Partial least-squares; Powdery mildew; Regression coefficient; Regression coefficient vector; Spectral signature; Support vectors machine; Germination

Digital Object Identifier (DOI)

• 10.1109/ROPEC55836.2022.10018618

PubMed ID

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