Machine Learning for Plant Disease Detection: An Investigative Comparison Between Support Vector Machine and Deep Learning

Aliyu Muhammad Abdu, Musa Mohd Muhammad Mokji, Usman Ullah Ullah Sheikh


Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of these variabilities are attributed to diseases caused by pathogens, and 60% to 70% of those diseases appear on the leaves in comparison to the stem and fruits.  This work provides a comparative investigation through model implementation of the performance of two renowned machine learning models; the Support Vector Machine (SVM) and Deep Learning (DL) for plant disease detection through their leaf images. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as “shallow-structured” machine learning architectures.  The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis.  Regardless, there is a lack of studies concerning its application in plant leaves disease detection.  Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security.


Image Processing; Machine Learning; SVM; Deep Learning; Plant Disease Detection


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