OSPS-MicroNet: a distilled knowledge micro-CNN network for detecting rice diseases

Abstract

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More than half of the world's population relies on rice as their primary food source. In India, it is a dominant cereal crop that plays a significant role in the national economy, contributing to almost 17% of the GDP and engaging 60% of the population. Still, the agricultural sector faces numerous challenges, including diseases that can cause significant losses. Convolutional neural networks (CNNs) have proven effective in identifying rice diseases based on visual characteristics. However, CNNs require millions of parameters, resulting in high computational complexity, so deploying these models on limited-resource devices can be difficult due to their computational complexity. In this research, a lightweight CNN model named Oryza Sativa Pathosis Spotter (OSPS)-MicroNet is proposed. OSPS-MicroNet is inspired by the teacher-student knowledge distillation mechanism. The experimental results demonstrate that OSPS-MicroNet achieves an accuracy of 92.02% with only 0.7% of the network size of the heavyweight model, RESNET152. This research aims to create a more streamlined and resource-efficient model to detect rice diseases while minimizing demands on computational resources.

Keywords

• convolutional neural network (CNN)
• rice leaf diseases
• ResNet152
• MobileNet
• OSPS-MicroNet
• computational complexity