Your search

Department
  • Tomato plant diseases pose a significant threat to agricultural productivity, resulting in substantial economic losses. Early and accurate diagnosis is crucial for effective disease management. This paper describes the design and implementation of expert systems for tomato disease detection using the CLIPS (C Language Integrated Production System) platform. The tool is designed to help farmers and agronomists accurately identify diseases affecting tomato crops by simulating knowledge from professional experts. We carefully developed a set of rules to distinguish leaf blight symptoms from those of other tomato diseases and provided recommendations to minimize crop losses and maximize yields. The expert system was developed using a forward-chaining inference engine, and its performance was evaluated through a set of real-world test cases, demonstrating a high level of accuracy and consistency in decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

  • With the increasing interest in natural language processing, text summarization has become essential for condensing large volumes of data into concise and meaningful summaries. Extractive summarization, which involves selecting key sentences based on textual features, has gained attention due to its efficiency and effectiveness. This research explores extractive summarization using multiple machine learning classifiers, including Support Vector Machines (SVM), Logistic Regression (LR), Decision Trees (DT), K-Nearest Neighbors (KNN), and Random Forest (RF). Our findings indicate that the Random Forest model achieved the highest accuracy, reaching 80% in classifying sentences for summary generation. Additionally, we evaluated text classification on the same BBC dataset using ChatGPT, which attained an accuracy of 62%. Furthermore, comparisons with results from prior research confirm the competitive performance of our approach, reinforcing the potential of machine learning models in extractive summarization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Last update from database: 4/24/26, 4:15 PM (UTC)

Explore

Department

Resource type

Resource language