Deep learning-based visual diagnosis of Huanglongbing and zinc deficiency in citrus leaves for sustainable and precision orchard management.

Citrus greening or Huanglongbing (HLB) is a major threat to global citrus production, yet its visual symptoms often overlap with those of zinc (Zn) deficiency, complicating accurate diagnosis and disease management. This study presents a biologically validated, image-based deep learning method for distinguishing HLB, Zn deficiency, and their co-occurrence in citrus leaves. A total of 2,500 leaves were collected from commercial orchards and imaged using high-resolution flatbed scanning. Five independent expert observers visually classified each leaf into four categories: healthy, HLB, Zn deficiency, and combined HLB and Zn. All samples were subjected to PCR analysis to confirm HLB infection. Comparison between expert assessment and PCR results revealed substantial inter-observer variability and an overall expert diagnostic accuracy of 80.5%, highlighting the inherent limitations of symptom-based visual diagnosis. A hybrid labeling strategy integrating PCR validation for HLB and expert-defined phenotypic evidence for Zn deficiency was used to generate a class-balanced dataset of 1,600 leaves. A multi-label convolutional neural network (CNN) trained on this dataset demonstrated strong diagnostic performance on a PCR-validated validation set, achieving F1-scores of 0.928 (Healthy), 0.936 (HLB), and 0.929 (Zn deficiency), with a Macro-F1 score of 0.931 and an exact-match accuracy of 0.876. Misclassifications were largely confined to physiologically related classes, consistent with overlapping symptom expression. These results demonstrate that deep learning models trained on molecularly validated ground truth can outperform visual diagnosis and serve as scalable decision-support tools for citrus disease management. By enabling more accurate differentiation between HLB infection and nutrient deficiency, this approach can reduce unnecessary or incorrect chemical applications in orchards. Consequently, it supports more precise crop management practices and contributes to sustainable agriculture and environmentally responsible plant protection.