Crop diseases represent a critical threat to global food security, demanding early and accurate detection to mitigate devastating losses. While AI offers immense potential, its adoption in agriculture is hampered by farmers' data privacy concerns and a lack of trust in "black box" models. Existing solutions combining federated learning for privacy and explainable AI (XAI) for transparency are a step forward, but they remain passive, offering one-way explanations to the user. This research introduces a paradigm-shifting framework, Symbiotic AI, that moves beyond passive explanation to active, collaborative intelligence. We propose a novel Federated Co-learning system where farmers and AI models learn from each other in a continuous, privacy-preserving loop. The core of this framework is an Interactive Counterfactual Explanation module. Instead of merely highlighting what the model saw (e.g., via heatmaps), our system uses a generative model (GAN) to show the farmer a counterfactual: a synthetic image of their own crop, subtly altered to show the minimal change required to flip the model's diagnosis (e.g., "This is what your healthy leaf would need to look like to be classified as having blight").

Crucially, the farmer can then interact with this explanation, confirming its accuracy or providing corrective feedback, such as, "No, the key indicator you missed is this stem discoloration." This expert human feedback is quantified and integrated back into the federated learning process, directly refining not only the central predictive model but also the local generative models that create the explanations. This creates a powerful co-learning dynamic where the AI becomes personalized to the unique environmental conditions and tacit knowledge of each participating farm, without ever compromising data sovereignty. The methodology involves a dual-model federated architecture—a Convolutional Neural Network (CNN) for disease detection and a conditional Generative Adversarial Network (cGAN) for generating counterfactuals—trained across decentralized farm data. Rigorous privacy is ensured through differential privacy. Expected outcomes include a system that not only achieves state-of-the-art detection accuracy (>97%) but also demonstrably improves over time by codifying farmer expertise. This research pioneers a new class of human-in-the-loop AI systems that fosters deep trust, accelerates adoption, and creates a dynamically evolving, resilient, and truly farmer-centric digital agriculture ecosystem.