Abstract:
Mango trees are tropical and subtropical trees that flourish in warm climates.
It is a popular, tasty fruit as well as a cash crop. Farmers have a hard time
selling their products when their output is reduced owing to diseases that affect
mango trees. To improve quality and production, it's vital to address any
harmful illnesses as soon as possible. This problem prompted the development
of novel technologies for detecting and diagnosing mango plant diseases, as well
as expert systems for disease prevention. Three machine learning techniques
are employed to detect mango diseases in this paper. A dataset with 20
different classes of infected and healthy mango fruit and leaf photos have been
created. Among these machine Learning methods, DenseNet169 obtains the
highest accuracy of 97.81%, with precision, recall, and F1-scores of 97%, 96%,
and 96%, respectively. An Android app has been developed and coupled with
the machine learning model that aids in the identification of mango illness as
well as the recommendation of pesticides based on disease detection.
