Tai-hoon Kim ¹ Mobeen Shahroz ² Bayan Alabdullah ³ Nisreen Innab ⁴ Jamel Baili ⁵ Muhammad Umer ² Imran Ashraf ^6*

• ¹ Chonnam National University, Gwangju, Gwangju, Republic of Korea
• ² Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan
• ³ Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
• ⁴ University of Almaarefa, Riyadh, Saudi Arabia
• ⁵ King Khalid University, Abha, Saudi Arabia
• ⁶ Yeungnam University, Gyeongsan, Republic of Korea

Leaf disease detection is critical in agriculture, as it directly impacts crop health, yield, and quality. Early and accurate detection of leaf diseases can prevent the spread of infections, reduce the need for chemical treatments, and minimize crop losses. This not only ensures food security but also supports sustainable farming practices. Effective leaf disease detection systems empower farmers with the knowledge to take timely actions, leading to healthier crops and more efficient resource management. In an era of increasing global food demand and environmental challenges, advanced leaf disease detection technologies are indispensable for modern agriculture. This study presents an innovative approach for detecting pepper bell leaf disease using an AFNIS Fuzzy convolutional neural network (CNN) integrated with local binary pattern (LBP) features. Experiments involve using the models without LBP, as well as, with LBP features. For both sets of experiments, the proposed ANFIS CNN model performs superbly. It shows an accuracy score of 0.8478 without using LBP features while its precision, recall, and F1 scores are 0.8959, 0.9045, and 0.8953, respectively. Incorporating LBP features, the proposed model achieved exceptional performance, with accuracy, precision, recall, and an F1 score of higher than 99%. Comprehensive comparisons with state-of-the-art techniques further highlight the superiority of the proposed method.1 Kim et al.Additionally, cross-validation was applied to ensure the robustness and reliability of the results. This approach demonstrates a significant advancement in agricultural disease detection, promising enhanced accuracy and efficiency in real-world applications.