arXiv:2601.07957v1 Announce Type: new 
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.

LWMSCNN-SE هو شبكة عصبية تلافيفية خفيفة الوزن مقترحة حديثًا لتصنيف أمراض الذرة بكفاءة، حيث تحقق دقة تصل إلى 96.63% مع متطلبات حسابية منخفضة، مما يجعلها مناسبة للنشر على الأجهزة الطرفية مثل الهواتف الذكية والطائرات بدون طيار.

LWMSCNN-SE es una nueva red neuronal convolucional ligera propuesta para la clasificación eficiente de enfermedades del maíz, logrando una precisión del 96.63 % con requisitos computacionales mínimos, lo que la hace adecuada para su implementación en dispositivos de borde como teléfonos inteligentes y drones.

LWMSCNN-SE est un nouveau réseau de neurones convolutifs léger proposé pour la classification efficace des maladies du maïs, atteignant une précision de 96,63 % avec des exigences computationnelles minimales, ce qui le rend adapté au déploiement sur des appareils en périphérie tels que les smartphones et les drones.

LWMSCNN-SE is a newly proposed lightweight convolutional neural network designed for efficient maize disease classification, achieving 96.63% accuracy with minimal computational requirements, making it suitable for deployment on edge devices like smartphones and drones.

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

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