Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations

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