A Comparative Study on Plant Disease Detection Using Machine Learning Algorithm


  • Muhammad Solihin Kadir UTM
  • Syahid Anuar




Crop diseases, Deep learning, Convolutional Neural Network, Machine Learning, Support Vector Machine, K-Nearest Neighbor


The crop diseases are major problem in agriculture industry that requires an accurate and fast crop disease detection method to prevent and limiting major loss. Many researchers utilize machine learning algorithm to achieve this solution. Majority of the solution either using traditional machine learning algorithm or deep learning-based algorithm. For traditional machine learning algorithm, the algorithm usually used feature extraction algorithm paired with machine learning algorithm such as Support Vector Machine, Logistic Regression and K-Neighbors. Deep learning-based algorithm utilize either fully connected neural network or use convolution neural network as feature extractor and paired it with machine learning classifier. However, evaluating those algorithms are quite difficult due to different settings in each experiment done in evaluating deep learning-based algorithm and traditional machine learning based algorithm. The purpose of this paper is to evaluate those algorithms with same dataset which is Plant Village dateset to give them fair comparison in performance. The results show that both machine learning and deep learning algorithm achieve great result with the highest accuracy achieve around 97% accuracy.




How to Cite

Kadir, M. S., & Anuar, S. (2022). A Comparative Study on Plant Disease Detection Using Machine Learning Algorithm . Open International Journal of Informatics, 10(2), 117–123. https://doi.org/10.11113/oiji2022.10n2.217