A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices

Abstract

One of the essential aspects of smart farming and precision agriculture is quickly and accurately identifying diseases. Utilizing plant imaging and recently developed machine learning algorithms, the timely detection of diseases provides many benefits to farmers regarding crop and product quality. Specifically, for farmers in remote areas, disease diagnostics on edge devices is the most effective and optimal method to handle crop damage as quickly as possible. However, the limitations posed by the equipment’s limited resources have reduced the accuracy of disease detection. Consequently, adopting an efficient machine-learning model and decreasing the model size to fit the edge device is an exciting problem that receives significant attention from researchers and developers. This work takes advantage of previous research on deep learning model performance evaluation to present a model that applies to both the Plant-Village laboratory dataset and the Plant-Doc natural-type dataset. The evaluation results indicate that the proposed model is as effective as the current state-of-the-art model. Moreover, due to the quantization technique, the system performance stays the same when the model size is reduced to accommodate the edge device.

Keywords

• Deep Convolution Neuron Networks
• Edge Computing
• Multi-leaf disease image
• Plant-Doc dataset