LWMSCNN-SE: A Lightweight Multi-Scale Network for Efficient Maize Disease Classification on Edge Devices

TL;DR

LWMSCNN-SE is a lightweight, efficient CNN model that accurately classifies maize diseases with low computational requirements, suitable for deployment on resource-limited edge devices like smartphones and drones.

Contribution

The paper introduces LWMSCNN-SE, a novel lightweight CNN architecture combining multi-scale features, depthwise separable convolutions, and SE mechanisms for efficient maize disease classification.

Findings

Achieves 96.63% accuracy with only 241,348 parameters

Operates at 0.666 GFLOPs, suitable for real-time edge deployment

Demonstrates high accuracy-efficiency trade-off in field conditions

Abstract

Maize disease classification plays a vital role in mitigating yield losses and ensuring food security. However, the deployment of traditional disease detection models in resource-constrained environments, such as those using smartphones and drones, faces challenges due to high computational costs. To address these challenges, we propose LWMSCNN-SE, a lightweight convolutional neural network (CNN) that integrates multi-scale feature extraction, depthwise separable convolutions, and squeeze-and-Excitation (SE) attention mechanisms. This novel combination enables the model to achieve 96.63% classification accuracy with only 241,348 parameters and 0.666 GFLOPs, making it suitable for real-time deployment in field applications. Our approach addresses the accuracy--efficiency trade-off by delivering high accuracy while maintaining low computational costs, demonstrating its potential for efficient maize disease diagnosis on edge devices in precision farming systems.