Behavior of Spikes in Spiking Neural Network (SNN) Model with Bernoulli for Plant Disease on Leaves

Spiking Neural Network (SNN) inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection, offering enhanced performance and efficiency in contrast to Artificial Neural Networks (ANN). Unlike conventional ANNs, which process static images without fully capturing the inherent temporal dynamics, our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification, integrating an encoding method to convert static RGB plant images into temporally encoded spike trains. Additionally, while Bernoulli trials and standard deep learning architectures like Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks (FCNNs) have been used extensively, our work is the first to integrate these trials within an SNN framework specifically for agricultural applications. This integration not only refines spike regulation and reduces computational overhead by 30% but also delivers superior accuracy (93.4%) in plant disease classification, marking a significant advancement in precision agriculture that has not been previously explored. Our approach uniquely transforms static plant leaf images into time-dependent representations, leveraging SNNs’ intrinsic temporal processing capabilities. This approach aligns with the inherent ability of SNNs to capture dynamic, time-dependent patterns, making them more suitable for detecting disease activations in plants than conventional ANNs that treat inputs as static entities. Unlike prior works, our hybrid encoding scheme dynamically adapts to pixel intensity variations (via threshold), enabling robust feature extraction under diverse agricultural conditions. The dual-stage preprocessing customizes the SNN’s behavior in two ways: the encoding threshold is derived from pixel distributions in diseased regions, and Bernoulli trials selectively reduce redundant spikes to ensure energy efficiency on low-power devices. We used a comprehensive dataset of 87,000 RGB images of plant leaves, which included 38 distinct classes of healthy and unhealthy leaves. To train and evaluate three distinct neural network architectures, DeepSNN, SimpleCNN, and SimpleFCNN, the dataset was rigorously preprocessed, including stochastic rotation, horizontal flip, resizing, and normalization. Moreover, by integrating Bernoulli trials to regulate spike generation, our method focuses on extracting the most relevant features while reducing computational overhead. Using a comprehensive dataset of 87,000 RGB images across 38 classes, we rigorously preprocessed the data and evaluated three architectures: DeepSNN, SimpleCNN, and SimpleFCNN. The results demonstrate that DeepSNN outperforms the other models, achieving superior accuracy, efficient feature extraction, and robust spike management, thereby establishing the potential of SNNs for real-time, energy-efficient agricultural applications.