HOG feature extraction in optimizing FK-NN and CNN for image identification of rice plant diseases

Abstract

This study compares the performance of FK-NN and CNN models in identifying rice diseases from digital images, focusing on both effectiveness and efficiency. Additionally, this research utilizes HOG for feature extraction from the digital images. The stages include data collection, preprocessing, transformation, classification, and model evaluation. The results show that the FK-NN model achieves a higher accuracy of 86.26%, compared to the CNN model's accuracy of 71.37%. Furthermore, the precision value of the FK-NN model is also higher at 86.88%, compared to the CNN model’s precision of 72.74%. Similarly, the recall value for the FK-NN model is higher at 86.88%, compared to the CNN model’s 71.37%. The F1-score of the FK-NN model is likewise superior, with a value of 86.88%, compared to the CNN model’s F1-score of 71.37%. These findings suggest that the FK-NN model with HOG feature extraction is more effective. However, in terms of inference time, the CNN model is faster, taking 0.000282 seconds compared to FK-NN’s 0.002331 seconds. In conclusion, the FK-NN model with HOG feature extraction excels in identifying rice diseases, while the CNN model offers faster inference time in this study.