Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, leaf scars, it challenging for farmers identify disease-prone conditions with traditional approaches. Thus, computerized model composed of vision, artificial intelligence, machine learning the demand age detect in plants efficiently. In this paper, we develop hybrid transfer (TL) simple CNN using small dataset detecting sunflower diseases. Out eight models tested on four different classes (downy fresh leaf), VGG19 + achieves best results terms precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, Cohen’s kappa metrics. The experimental outcomes show proposed provides better accuracy than other approaches benchmark dataset.