Maize Leaf Disease Identification with Large and Lightweight Convolutional Neural Models - Lye, Mohd Haris; Fauzi, Mohammad Faizal Ahmad; Lim, Kian Ming (JOIV : International Journal on Informatics Visualization, 2025)
To minimize yield losses in maize plantations, control measures that include early leaf disease detection are essential. In this study, we evaluated extensive and lightweight convolutional neural network (CNN) models to accurately classify maize diseases from leaf images. To achieve a high image classification performance, existing deep learning approaches often use large models that require substantial computational resources.  Simpler and lightweight models provide faster inferences but at the expense of lower accuracy in prediction performance. To improve maize leaf disease classification performance on the lightweight SqueezeNet model, the response-based knowledge distillation method was evaluated for model training. In response-based knowledge distillation, the logit output from the last layer of the large model is used in the loss function to train the lightweight model. This enables the lightweight model to learn from the knowledge of large and complex models, thereby improving its predictive accuracy while maintaining a simpler architecture and faster inference. A six-class maize disease dataset was prepared using two publicly available datasets. The dataset was used to train and evaluate the selected large and lightweight models. The large and lightweight model demonstrated high classification accuracy when trained till 40 epochs. The trained SqueezeNet model showed promising performance for accurately identifying various maize leaf diseases with an accuracy of 96.68%. When the model is trained with the response-based knowledge distillation method, the test accuracy improves to 97.13%. Such lightweight models with high accuracy can facilitate the deployment on resource-constrained devices.