Fuzzy-Augmented Neural Network for Reducing Computational Complexity: A Demonstration in Microclimate Prediction for Smart Agriculture

Climate change poses significant challenges, particularly in agriculture, where extreme weather events demand more efficient and resilient systems, such as smart greenhouses. These controlled environments require predictive solutions to optimize conditions, such as temperature and humidity, which are critical for crop growth. While neural networkss (NNs) are widely employed for climate prediction, their complexity presents a barrier to implementation on edge devices, which are characterized by limited computational resources. In this work, we propose fuzzy-augmented neural network (FANN), a novel approach based on fuzzy sets, applied in cascade to regressive NN models, to reduce complexity and energy consumption in greenhouse microclimate classification and prediction. The methodology was tested on four edge devices, including microcontrollers and microprocessors. We compare our FANN approach with standard models [feedforward neural networks (FFNN), binarized neural networks (BNN), simple recurrent neural networks (SimpleRNN), gated recurrent units (GRU), long short-term memory (LSTM)], highlighting significant reductions in inference time, energy consumption, and memory usage. FANN also offers practical advantages, such as the ability to adapt classification by modifying fuzzification parameters without retraining the model, and the potential to parallelize computations for simultaneously classifying the microclimate of multiple crops. These features make the system flexible and optimal for practical applications in dynamic agricultural contexts.