Potato Plant Disease Evaluation Expert System Using Case-Based Reasoning K-Nearest Neighbor Algorithm

Potatoes is an agricultural commodity that serves as a substitute for staple food. All kinds of ways have been done to increase the productivity of potato plants, but obstacles encountered in the process of planting potatoes include the presence of diseases that often result in crop failure. Lack of knowledge of farmers and the public about the types of diseases contained in potato plants, resulting in crop failure. Accurate and timely diagnosis of these diseases is essential for effective management and control. This paper presents the development of an expert system to diagnose diseases in potato plants using the Case Based Reasoning (CBR) method combined with the K-Nearest Neighbor (K-NN) algorithm. The system utilizes a database of past cases to identify and diagnose diseases based on the similarity between new cases and existing cases. The integration of CBR with K-NN algorithm improves the accuracy and reliability of diagnosis by considering various symptom features and environmental conditions. The results show that the system achieves a high level of accuracy in diagnosing potato plant diseases, outperforming traditional methods. This research aims to develop an efficient and easy-to-use tool for farmers and agricultural professionals to facilitate early detection and management of potato crop diseases, while improving the system performance metrics, including accuracy, precision, recall, and F1-Score, to assess the effectiveness of the system diagnostics for potato crop diseases. The contribution of this research aims to offer an easy accessible option for farmers to quickly identify and manage diseases in potato plants, thereby reducing losses due to crop failure. A future work will focus on expanding the system database and incorporating additional Machine Learning (ML) techniques to further improve diagnostic capabilities.