Abstract:
In coastal areas where agriculture is often constrained by salinity, cultivating specific
crops like Luffa Aegyptiaca (sponge gourd) becomes crucial for local sustenance.
Identifying these diseases is challenging and time-consuming when no domain specialists
are present accurately, and the information needs to be more consistent. Effective disease
detection and management play a pivotal role in ensuring the viability of these limited yet
vital crops, impacting crop yield, fertilization strategies, and overall food security for
coastal communities. This groundbreaking study focuses on detecting and classifying leaf
diseases within Luffa Aegyptiaca leaves, prevalent crops in coastal regions. Leveraging
the cutting-edge capabilities of Convolutional Neural Networks (CNN) and Vision
Transformer algorithms, our research achieves unparalleled accuracy. The CNN
algorithm boasts an impressive accuracy of 98.32%, while the Vision Transformer
algorithm surpasses expectations with an exceptional accuracy of 99.85%. Notably, this
study utilizes an original dataset, a unique contribution to the field given the absence of
publicly available datasets or prior research specific to Luffa Aegyptiaca. Beyond mere
accuracy metrics, our findings illuminate profound insights into the nuanced landscape of
leaf disease detection and classification, affirming the remarkable efficacy of both CNN
and Vision Transformer algorithms. In conclusion, this research advances our
understanding of plant pathology, and underscores the unparalleled potential of state-ofthe-art machine-learning techniques in agricultural research.
