A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features
?????? ?????????? ??????????????????!?
We are proud to announce the publication of a recent, official paper by
Quick summary:?This study explores the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. The Artificial Neural Network outperforms other classifiers, achieving an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. The results show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in establishing tomato leaf disease classification systems and may facilitate the implementation of preventive measures by farmers, ultimately leading to enhanced yield quality and quantity.
Link to the paper:?https://www.cell.com/heliyon/fulltext/S2405-8440(23)08905-3?
Find more related publications at:
Machine Learning and Data Analytics:?https://sdas-group.com/machine-learning/
All publications:?https://sdas-group.com/allpublications/