A Cloud Edge based Intelligent System for Detection of Grape Diseases

Abstract

SegmentatFinding plant diseases early on is essential for reducing damage while improving the quality of the yield. This paper describes an intelligent cloud-edge-based system for identifying and categorizing diseases in grape plants. The system is based on the support vector machine (SVM), a supervised machine learning technique to classify data. The traits of healthy and diseased plants are identified using digital photographs of grape plants. To determine color and texture information, we retrieved global features from the grape images. Patterns or structures (such as corners or edges) are discovered using the speeded-up robust features (SURF) method. The K-means clustering approach is used to quantify feature space, which lowers the number of feature descriptors. The training set for the SVM classifier is made up of feature descriptors. The system classified the unlabeled grape images using the trained SVM classifier during testing. 1600 RGB pictures from four classes: Black-rot, Black-measles, Leaf-blight, and Healthy-leaf make up the original data set. To evaluate the system and provide accuracy and confusion matrices, simulations are run in four different color spaces (grayscale, RGB, YCbCr, and L*a*b*). In the L*a*b* color space, the system attained a maximum average accuracy of up to 90.63% at a ratio of 70:30 training to testing data.

Code and data availability

The paper's grape disease classification uses the public PlantVillage grape leaf image dataset from Kaggle, and the authors also deposited their selected 1600-image dataset on Figshare with an explicit availability statement. No author analysis code or trained model is shared.