Date of Award


Degree Name

MS in Computer Science


Computer Science


College of Engineering


Franz Kurfess

Advisor Department

Computer Science

Advisor College

College of Engineering


In the United States alone, common diseases spread among plants account for billions of dollars lost in crop yield each year. This issue is exacerbated in countries with less infrastructure to defend against crop epidemics, and can lead to famine and forced migration. Farmers can seek the help of plant pathology experts to defend against diseases and detect crop irregularities early on. However, access to experts can be difficult, and even those trained in the field may miss symptoms before it is too late. To assist in early disease detection, a number of papers have been released on the potential for machine learning image classifiers to identify healthy plants from infected ones using convolutional neural networks. While these papers are promising, they often fail to implement a set of standardized practices in their model implementation or make use of realistic data sets.

This thesis outlines a set of best practices to use when creating a convolutional neural network for plant disease detection. These principles were selected through a combination of related work analysis and generalized best practices on machine learning. A selection of 11 research articles that discuss their own plant disease image classifiers are analyzed on the grounds of these principles to assess their validity. Then, to demonstrate these principles in practice, we trained six models that each follow our set of guidelines to distinguish healthy strawberry plant images from diseased ones. While the focus of our paper centers on the need to use these practices to create field-realistic models, we achieved the best results on our strawberry image classifier using a VGG16 model architecture. We hope that this work will inspire a set of standardized practices to follow when developing a plant disease image classifier, and allow for more accurate model comparisons in the future.