Fungal Blast Disease Detection in Rice Seed using Machine Learning

The economy of Pakistan mainly relies upon agriculture alongside other vital industries. Fungal blast is one the significant plant diseases found in rice crops, leading to reduction agricultural products and hindrance country's economic development. Plant disease detection an initial step towards improving yield quality products. Manual Analyzation health tiresome, time taking costly. Machine learning offers alternate inspection method providing benefits automated inspection, ease availability, cost reduction. visual patterns on plants are processed using machine classifiers such as support vector (SVM), logistic regression, decision tree, Naïve Bayes, random forest, linear discriminant analysis (LDA), principal component (PCA), based classification results recognized healthy or unhealthy. For this process, a dataset containing 1000 images seed crop collected from different fields Kashmore, whole image acquisition, pre-processing, feature extraction done only. annotated with unhealthy samples help expert. algorithms used for processing data evaluated terms F1-score testing accuracy. This paper contains traditional classifiers, these transfer has been compare results. Finally, comparative between deep networks.