An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability

Environmental deterioration and climate change significantly affect agricultural productivity and food security.Existing solutions in precision agriculture are based mostly on black-box artificial intelligence (AI) models, which do not offer much interpretability and are not flexible enough to account for changes in climatic conditions.In order to overcome these shortcomings, this work develops an automata-driven cognitive explainable AI framework for climate-adaptive precision agriculture and environmental sustainability.The developed framework combines cognitive approaches to machine learning and finite-state automata in order to develop predictive models capable of adapting to changing environmental conditions and guiding agricultural decision-making.Climate, soil, and crop data can be fed into predictive intelligent models to evaluate crop suitability, potential yields, irrigation needs, and climate threats.Moreover, techniques of explainable AI, such as SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations, will be employed to ensure greater transparency in model operation.The sustainability of farming activities will be assessed in terms of resource consumption.