Survey of Accuracy Prediction on the PlantVillage Dataset using different ML techniques

Abstract

A plant is susceptible to numerous illnesses while it is growing. The early detection of plant illnesses is one of the most serious problems in agriculture. Plant disease outbreaks may have a remarkable impact on crop yield, slowing the rate of the nation's economic growth. Early plant disease detection and treatment are possible using deep learning, computer-vision, and ML techniques. The methods used for the categorization of plant diseases even outperformed human performance and conventional image-processing-based methods. In this context, we review 48 works over the last five years that address problems with disease detection, dataset properties, the crops under study, and pathogens in various ways. The research results discussed in this paper, with a focus on work published between 2015 and 2023, demonstrate that among numerous techniques (MobileNetV2, K-Means+GLCM+SVM, Residual Teacher-Student CNN, SVM+K-Means+ANN, AlexNet, AlexNet with Learning from Scratch, AlexNet with Transfer Learning, VGG16, GoogleNet with Training from Scratch, GoogleNet with Transfer Learning) applied on the PlantVillage Dataset, the architecture AlexNet with Transfer Learning identified diseases with the highest accuracy.