TY - JOUR
T1 - Evaluating plant disease detection mobile applications
T2 - Quality and limitations
AU - Siddiqua, Ayesha
AU - Kabir, Muhammad Ashad
AU - Ferdous, Tanzina
AU - Ali, Israt Bintea
AU - Weston, Leslie A.
N1 - Funding Information:
However, 35.30% (6/17) of the apps contain informative comments and have 10K+ to 10M+ installations. A word cloud is generated in a using the positive comments collected from the selected apps. One of the most optimistic collections of user comments has been noticed for Plantix-your crop doctor . This app has already received 60K+ comments, with 65% being 5-star reviews. Among the 35K+ highest star rating comments, people mentioned the good points of this app. Users have used several words to express their satisfaction, such as “helpful,” “wonderful,” “amazing,” “fantastic,” “awesome,” “very good,” “great,” “best,” “excellent,” “useful,” “easy to use,” “accurate,” “nice,” and many more. Many users gave “thanks” and praised the expert team of this app community. According to some users, the finest plant disease diagnosis app is Plantix–your crop doctor . The app has several wonderful features, such as detection ability for crop diseases, a fertilizer calculator, and pest and disease-related information with high-resolution pictures. The app provides community support where plant lovers can share plant-related problems and get suggestions from other users and experts. One user has commented “Hello team plantix You people are doing wonderful job... I personally loved this app this app gives complete guidance for farming a variety of crops. Gives details not only about disease but also about its treatment also provide guidance about fertilization of crop. It would be great if more people installed and gave it a shot you will not regret installing it. Best luck team plantix and thanks a lot.” This comment is supported by many users. Another app, Agrio , has received the second-highest number of positive reviews from users.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/8
Y1 - 2022/8
N2 - 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, which includes providing ratings of distinct features of the apps and insights into the exploitation of artificial intelligence used in plant disease detection. The applicability of these apps for pathogen or disease detection, identification, and treatment will be assessed along with significant insights garnered. For this purpose, plant disease detection apps were searched in three prominent app stores (the Google Play store, Apple App store, and Microsoft store) 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. To validate the rater agreements on the ratings, inter-rater reliability is computed alongside their intra-rater reliability, ensuring their rating consistency. Also, the internal consistency of our rating scale was evaluated against all selected apps. 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.
AB - 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, which includes providing ratings of distinct features of the apps and insights into the exploitation of artificial intelligence used in plant disease detection. The applicability of these apps for pathogen or disease detection, identification, and treatment will be assessed along with significant insights garnered. For this purpose, plant disease detection apps were searched in three prominent app stores (the Google Play store, Apple App store, and Microsoft store) 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. To validate the rater agreements on the ratings, inter-rater reliability is computed alongside their intra-rater reliability, ensuring their rating consistency. Also, the internal consistency of our rating scale was evaluated against all selected apps. 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.
KW - plant disease
KW - pathogen
KW - artificial intelligence
KW - disease detection
KW - mobile app
KW - smartphone
KW - app design
KW - machine learning
UR - https://www.mdpi.com/2073-4395/12/8/1869
UR - http://www.scopus.com/inward/record.url?scp=85137337884&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137337884&partnerID=8YFLogxK
U2 - 10.3390/agronomy12081869
DO - 10.3390/agronomy12081869
M3 - Article
SN - 2073-4395
VL - 12
JO - Agronomy
JF - Agronomy
IS - 8
M1 - 1869
ER -