Research Information System
Computers and Electronics in Agriculture, vol.239, 2025 (SCI-Expanded, Scopus)
Accurate identification of plant diseases is critical for maximizing agricultural productivity, particularly in high-value crops like Peruvian coffee, where traditional manual diagnostics remain error-prone and inefficient. While numerous studies have explored convolutional neural networks (CNNs) for disease classification, achieving optimal performance hinges on the precise tuning of hyperparameters a process often relegated to suboptimal trial-and-error methods. Using metaheuristic optimization algorithms to detect such hyperparameters would be a correct approach. For this reason, with the artificial bee colony (ABC) optimization method was goal to determine the hyper-parameters of the proposed CNN architecture in the study. In accordance with this goal, both Peruvian Coffea Dataset (CoLeaf-DB), which is an up-to-date dataset, and Arabica Coffee Leaf Dataset (AcLeaf-DB) which is a reliable dataset with which many studies have been conducted on this subject and which can be benchmarked, were used. On CoLeaf-DB, which is a current dataset used in the study, values of 0.94, 0.94, 0.94, 0.95 were obtained in terms of precision, recall, F1 score, accuracy performance metrics, respectively. Same to order, the values of 0.95, 0.95, 0.95, and 0.97 were obtained from the AcLeaf-DB. When the obtained values are compared with state-of-the-art (SOTA) studies, it is revealed that the determination of hyperparameters with the proposed optimization method and the CNN-based architecture developed on this basis have an extremely important effect on disease detection.