Detecting mildew diseases in cucumber using image processing technique [electronic resource].

Includes bibliographic reference.

In Egypt, Cucumber is a crucial cash crop, and its farming
could significantly benefit the country's agriculture-based
economy. Meanwhile plant disease detection manually is costly
and time consuming. This study aims to improve early
identification of downy and powdery mildew diseases using
Keywords:Cucumber; Downy Mildew; Powdery Mildew; Machine Learning; Image Processing.
machine vision by comparing the effectiveness of several
detection methods and developing a real life application. This
approach will involve five steps including: image acquisition,
pre-processing, feature extraction, post-processing, and
classification. In which the experiment was conducted on two
greenhouses where 931 images were obtained and used with
five key features to train and evaluate the proposed methods.
The classification performance of three machine learning
algorithms, named discriminant analysis (DA), support vector
machine (SVM) and K‑nearest neighbors (KNN), were
compared. The results indicated that the fine gaussian SVM
achieved the highest classification accuracy rate of 96%, where
fine KNN got 95.8%, and quadratic DA obtained the lowest
value 92.8%. Additionally, the suggested method has a
practical application that enables automatic mildew disease
detection via personal computers, eliminating the need for
sample collection and laboratory analysis. This method could
also be extended to identify other plant diseases and pests and
track disease progression as the study moves forward.

Summary in Arabic.