  In this technologically advanced era, with the proliferation of artificial
intelligence, many mobile apps are available for plant disease detection,
diagnosis, and treatment, each with a variety of features. These apps need to
be categorized and reviewed following a proper framework that ensures their
quality. This study aims to present an approach to evaluating plant disease
detection mobile apps, this includes providing ratings of distinct features of
the apps and insights into the exploitation of artificial intelligence used in
plant disease detection. For this purpose, plant disease detection apps were
searched in three prominent app stores using a set of keywords. A total of 606
apps were found and from them 17 relevant apps were identified based on
inclusion and exclusion criteria. The selected apps were reviewed by three
raters using our devised app rating scale. User comments from the app stores
are collected and analyzed to understand their expectations and views.
Following the rating procedure, most apps earned acceptable ratings in software
quality characteristics such as aesthetics, usability, and performance, but
gained poor ratings in AI-based advanced functionality, which is the key aspect
of this study. However, most of the apps cannot be used as a complete solution
to plant disease detection, diagnosis, and treatment. Only one app, Plantix -
your crop doctor, could successfully identify plants from images, detect
diseases, maintain a rich plant database, and suggest potential treatments for
the disease presented. It also provides a community where plant lovers can
communicate with each other to gain additional benefits. In general, all
existing apps need to improve functionalities, user experience, and software
quality. Therefore, a set of design considerations has been proposed for future
app improvements.
