Abstract
The detection of plant diseases in the modern era offers a promising first step toward sustainable agriculture and food security. Plant physiology can be studied quantitatively thanks to advances in imaging and computer vision. Conversely, manual interpretation requires a great deal of labor, knowledge of plant diseases. Numerous innovative methods for identifying and classifying particular diseases have been widely used. In order to diagnose potato diseases more accurately and quickly using a machine learning model, this study uses a new feature extraction method based on GJPs image features. The methodology of this study relies on:•Modules for preprocessing, feature extraction, dimension reduction, and classification.•Generalized jones polynomials as new image features method is used to extract the texture features from potato images for diagnosing potato diseases. The data used in this model were collected from the plant village image dataset using samples of potato leaves. Using an SVM classifier on potato leaf images, the disease was accurately identified in 98.45 % of cases. The recommended feature extraction technique can reduce financial loss while also assisting in the efficient management of plant diseases, enhancing crop productivity and ensuring food security.