Abstract

An Enhanced Intelligent Information System (EIIS) that uses plant leaf images is increasingly applied in agriculture to identify plant leaves diseases. That is processed on feature extraction and classification hybrid model with its techniques that have been developed for this purpose, and their performance is typically evaluated from the developers’ point of view. However, this study aims to assess the effectiveness of EIIS in detecting image based plant leaves diseases detection from the perspective of the users. That is an AI-based Intelligent Information System that integrates artificial intelligence techniques, data processing, and user interface components to deliver effective decision support, where information systems are used to guide the health of the crops. These systems have enabled the advancement of smart farming by improving productivity, precision, and resource efficiency through data-driven insights. This study proposes a hybrid approach for plant disease detection that combines Convolutional Neural Networks (CNN) for feature extraction and Weighted K-Nearest Neighbors (KNN) algorithm is used for classification. The CNN model efficiently extracts discriminative features from leaf images, while the Weighted KNN classifier assigns greater importance to closer data points, enhancing classification accuracy. The proposed method is applied to potato plant leaf disease recognition and achieves an accuracy of 98.75%. Furthermore, the system is implemented with a Tkinter-based graphical user interface, ensuring user-friendly interaction and practical usability. The overall framework demonstrates high accuracy, efficiency, and accessibility, making it a reliable intelligent decision support system for real-world agricultural applications.

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