Multi-Class Sunflower Disease Detection by Integrating Enhanced Jellyfish Search Algorithm

Objectives: This research aims to classify diseases observed in sunflower flowers and leaves using deep learning algorithms. The goal is to enhance agricultural output by addressing the significant issue of plant diseases, which negatively affect the security of the food supply. Methods: The study utilizes a feature selection approach to identify multi-class sunflower diseases. The Enhanced Jellyfish Search (EJFSOA) algorithm is improved in three ways: (i) optimization capabilities and convergence speed are enhanced through sine and cosine learning variables during Type B motion in the swarm, (ii) a local escape operator is added to avoid the local optimization trap, improving the exploitability of the JS algorithm, and (iii) opposition-based learning and quasi-opposition learning strengthen the population distribution. A deep learning model, Multi-class Sunflower Network (MCSFNet), combines ResNets with TensorFlow for feature extraction, multi-label classification, and a multi-spectral stacking module. Findings: The proposed model is evaluated using several criteria, including accuracy, sensitivity, specificity, and F1-score. On a publicly available dataset, the algorithm achieves an average accuracy of 99.56%, outperforming state-of-the-art approaches with accuracies of 98.34% and 97.39%. Novelty: This work introduces improvements to the EJFSOA algorithm, significantly enhancing the precision and convergence speed of sunflower disease classification models. Combining deep learning with an optimized feature selection method leads to superior results in multi-class classification tasks. Keywords: Sunflower Leaves, Disease Detection, Enhanced jellyfish search, Local escape operator, Multi­class Sunflower Network, Residual Networks