ABC+CNN-SH: Detection of peruvian coffea leaf diseases with a new hybrid classification algorithm based on ABC optimization and CNN

•ABC-based hyperparameter optimization outperforms traditional methods.•The innovative solution prevents yield losses due to mineral deficiencies in Peruvian coffee plants.•The proposed efficient CNN model detects nutrient deficiencies with minimal computational overhead.•The proposed ABC+CNN-SH has...

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Vydané v:Computers and electronics in agriculture Ročník 239; s. 111114
Hlavní autori: Çetiner, Halit, Metlek, Sedat
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2025
Predmet:
Artificial bee colony
CNN
Leaf diseases classification
Optimization
Precision agriculture
Precision agriculture
CNN
Artificial bee colony
Leaf diseases classification
Optimization
ISSN:0168-1699
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Shrnutí:•ABC-based hyperparameter optimization outperforms traditional methods.•The innovative solution prevents yield losses due to mineral deficiencies in Peruvian coffee plants.•The proposed efficient CNN model detects nutrient deficiencies with minimal computational overhead.•The proposed ABC+CNN-SH has been tested on various coffee datasets commonly used in the literature.•The study provides 7.25 % higher accuracy than existing literature benchmarks. 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.
ISSN:0168-1699
DOI:10.1016/j.compag.2025.111114