Abstract:
Allium Sativum is a widely used spice noted for its culinary, medical, and spiritual
purposes, including its roles as an antibacterial, anti-thrombotic, and antineoplastic
agent. It has been integral to human civilization for thousands of years. In Bangladesh,
Allium Sativum farming is economically significant, delivering major money to
farmers and creating employment opportunities. It also encourages agricultural variety,
and soil health lowers the hazards of monoculture through crop rotation. Allium
Sativum has nutritional and health benefits due to its antibacterial characteristics and
is a staple in Bangladeshi cuisine, assuring stable local demand and value addition
through processed products. This study focuses on the classification of eleven Allium
Sativum disorders using powerful deep-learning methods. The diseases classified
include Basal Rot (Fusarium culmorum), Bloat Nematode (Ditylenchus dipsaci),
Botrytis Rot (Botrytis porri), Bulb Mites (Rhizoglyphus spp--Tyrophagus spp), Bulb
and Stem Nematode (Ditylenchus dipsaci), Disease Free, Leek Yellow Stripe (genus
Potyvirus), Purple Blotch (Alternaria porri), Rust (Puccinia porri), and White Rot
(Sclerotium cepivorum). Various convolutional neural network (CNN) architectures
were employed, with Xception achieving the best test accuracy of 91.99%, exhibiting
superior ability in diagnosing certain Allium Sativum disorders. Other models
employed include MobileNetV2, InceptionV3, VGG16, and DenseNet121, each
contributing to the robust identification of Allium Sativum diseases. This research
emphasizes the potential of deep learning in strengthening agricultural practices by
enabling accurate and fast disease identification, eventually helping farmers and the
agricultural economy.
