Aurora-Felicia Cristea, Ciprian Dobre
Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited labels and dataset bias. Real-field complexities, such as environmental variability, heterogeneous varieties or temporal dynamics as are often overlooked. Numerous studies have been conducted to address these challenges, proposing advanced learning strategies and improved evaluation protocols. Synthetic data generation and self-supervised learning reduce dataset bias, while domain adaptation, hyperspectral, and thermal signals improve robustness across sites. However, a large portion of current methods are developed and validated mainly on clean laboratory datasets, which do not capture the variability of real-field conditions. Existing AI models often lead to imperfect detection results when dealing with field images complexities, such as dense vegetation, variable illumination or changing symptom expression. Although augmentation techniques can approximate real-world conditions, incorporating field data represents a substantial enhancement in model reliability. Federated transfer learning offers a promising approach to enhance plant disease detection, by enabling collaborative training of models across diverse agricultural environments, using in-field data but without disclosing the participants data to each others. In this study, we collaboratively trained a hybrid Graph–SNN model using federated learning (FL) to preserve data privacy, optimized for efficient use of participant resources. The model achieved an accuracy of 0.9445 on clean laboratory data and 0.6202 exclusively on field data, underscoring the considerable challenges posed by real-world conditions. Our findings demonstrate the potential of FL for privacy preserving and reliable plant disease detection under real field conditions.