Implementation of Digital Image Processing for Early Detection of Plant Leaf Diseases Using an AI Computing Architecture
Keywords:
Plant Disease Detection, Digital Image Processing, Convolutional Neural Network, Computational Efficiency, Edge ComputingAbstract
The application of Convolutional Neural Networks (CNNs) has demonstrated high accuracy in detecting plant diseases. However, most previous studies have primarily focused on improving predictive performance and have paid limited attention to the computational efficiency required to support real-time implementation in agricultural environments. Based on this condition, this study aims to design, implement, and evaluate digital image processing methods for the early detection of plant leaf diseases while considering the balance between predictive accuracy and hardware efficiency. The study employs a quantitative computational experimental design by training a CNN model using a standardized plant leaf image dataset. The experiments compare computational performance between a multi-core Central Processing Unit (CPU) and a Graphics Processing Unit (GPU) accelerator, while also analyzing the effect of image compression formats (BMP, PNG, and JPEG) on Input/Output (I/O) latency through the measurement of predictive and compu
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