Ant Colony Optimization Algorithm for Disease Detection in Maize Leaf using Machine Learning Techniques

  • ALOK KUMAR Government College of Engineering, Dharmapuri
Keywords: Supervised Machine learning algorithms, Hu moments, Haralick texture, Colour Histogram, Machine Learning Classifiers


Plant diseases affect the productivity of the food recent years. Because of this productivity loss, not only humans, animals are also affected but whole biodiversity would be affected. So, we should take the preventive measures to stop this food destruction. Maize crop is largely consumed by both humans and animals. Due to some factors, Maize leaf is easily affected by some fungal or other diseases. Farmers could not find out the leaf diseases at the early stages. They need some advanced methods to detect these types of diseases. Early detection of leaf disease helps farmers to increase the Maize yield. In proposed algorithm, we have used five supervised machine learning algorithms such as K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR) and then these machine learning models have been implemented with the Ant Colony Optimization (ACO) Algorithm for optimizing the accuracy of disease detection in Maize Leaf. For leaf classification, colour and texture feature are extracted from an input dataset. Features of a leaf can be descripted by Hu moments, Haralick texture and Colour Histogram. After performing all Machine learning classifiers, we have analyzed that Random Forest with Ant Colony Optimization Algorithm gives the highest accuracy of 99.4% for disease detection in Maize Leaf.


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