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 (Institute of Advanced Engineering and Science, 2020)
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 the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as