Plant microbial fuel cells (PMFCs), bio-photovoltaic arrays, and hybrid electrochemical platforms are all examples of new types of renewable energy systems that use living plants to generate a small but steady amount of electricity. These systems depend on the metabolic interactions between roots, soil microorganisms, and materials that can conduct electricity. But their performance is always uncertain because changes in the environment, biological variability, and limits on measurements make things less clear than classical deterministic models can show. Fuzzy logic as it is used now can show different levels of truth, but it combines ignorance and vagueness into one value, making it harder to understand in complicated natural systems. This study presents a conceptual and mathematical model based on neutrosophic logic that aims to define and measure uncertainty in plant-based electricity generation. Neutrosophic triplets (T,I,F) show key environmental and biological factors like solar radiation, soil moisture, temperature suitability, and plant vitality. This lets truth, indeterminacy, and falsity be shown separately. A rule-based inference mechanism puts these triplets together to make an interval-based guess about how much electricity will be produced. The work lays out the theoretical framework for this method and makes it clear how it can be used to model environmental energy. In general, the framework shows how neutrosophic reasoning gives us a better way to understand the uncertainty that comes with living green energy systems.