An Intelligent Edge-AI-Enabled Framework for Maize Disease Detection System Using Deep CNN Models

Abstract

Maize is the most common cereal crops cultivated and early identification of leaf diseases is the primary measure to avoid yield of the crop and food insecurity. The conventional ways of manual inspections are time consuming, subjective and expert based. The proposed work suggests an intelligent Edge-AI-based system of detecting any disease in maize employing deep Convolutional Neural Network (CNN) models. The proposed work combines image acquisition with mobile or edge sensors and on-device deep learning inference to provide the opportunity to detect diseases in real-time with minimum latency and minimum reliance on the cloud. An edge hardware is used to perform a lightweight, but high-precision CNN network optimized by both transfer learning and data augmentation methods. The model is conditioned to identify various maize leaf diseases and healthy samples with greater resilience to different environmental factors. The experimental results show that the tools are highly classifiable, inference time is very low and resource use is efficient. The proposed design provides scalable, affordable, and smart solution to precision agriculture and smart farming implementation.