# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 2 Related Work  
This section discusses the existing works in the literature concerning compression techniques for NNs, focusing on three approaches: network pruning, sparse representation, and knowledge distillation.  
Network pruning is one of the most commonly used traditional compression techniques. In particular, the authors of [17] proposed a channel pruning approach at inference time for Convolutional Neural Networks (CNNs), aiming to reduce the number of channelsin the inputs provided to the intermediate layers. VarGNet [18] represents another approach for determining the importance of channels and removing redundant ones. Another solution to search for the optimal configuration of channels to retain is Evolutionary Net Architecture Search (EvoNAS) [19]. Conversely, in [20], the authors remove connections between less important neurons, proposing an approach called DeepIoT, which is specifically designed for IoT devices. Meanwhile, one method that is used to estimate redundant weights during training is Sparse CNN [21]. Layer pruning, which involves removing entire layers from a DNN, has been employed in [22], where the authors compared the performance of various classification approaches against their own, compressing DNN models on embedded devices.  
Another way to compress a NN is by managing the sparsity of the weight matrix. Quantization of weights to a lower number of bits, as proposed in [23], allows a reduction in memory usage, and by incorporating integer arithmetic processing, Floating Point Operations (FLOPs) are executed more efficiently [24]. A comparison between pruning and quantization compression techniques was presented in [25], in which the authors evaluated the energy consumption on an ESP32 microprocessor, achieving 45% savings with quantization. An extreme quantization solution is binary quantization [26], using binary numbers as weights and logical operations. Another technique is to share similar-valued weights for each layer, storing only one and creating multiple connections [27]. Differently, the authors of [28] propose sharing similar weights not only within a single DNN layer, but also across multiple layers.  
Finally, knowledge distillation is a technique that transfers knowledge from a large and complex model to a smaller one, as shown in [29]. In [30], the authors demonstrate that the compressed model is more computationally and memory-efficient while achieving comparable accuracy to the original model. However, in [31], it was shown that a downside of knowledge distillation is overfitting, caused by the logits of the pre-trained model.  
In contrast, in this work, to achieve the same objective of complexity reduction while maintaining classification accuracy, we propose an integration between NNs and granular computing, specifically leveraging fuzzy logic. Our method is able to reduce the NN's complexity in terms of memory variables and FLOPs, as we will show in Section 5. In more detail, compared to a previous study [32], this work provides a more consistent comparison between two classifier models. Consequently, different metrics are used, specifically the _F\({}_{1}\)score_ instead of the Mean Absolute Percentage Error (MAPE), coefficient of determination (R\({}^{2}\)), and Mean Square Error (MSE). Additionally, the performance analysis was conducted by evaluating the computational complexity of the system in terms of memory-allocated variables and the number of FLOPs. Finally, this work implements the proposed system within a network architecture to enhance its applicability in real-world scenarios.