An Innovative Approach for Classifying Plant Diseases Using  Deep Learning

Authors

Ahmed Solyman School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK.
Ismail A. Elhaty Department of Nutrition and Dietetics, Faculty of Health Sciences, Istanbul Gelisim University, Istanbul, Turkey.

Deep learning, Classification, Agriculture.

The classification of plant diseases is essential for the agricultural industry to promptly identify and address

crop challenges. The numerous errors and inefficiencies in existing ailment identification approaches prompt the

advancement of enhanced methods. This study uses deep learning, namely the EfficientNet method, to strengthen the

classification of plant diseases. Through rigorous research and meticulous refinement, the EfficientNet model has been

optimized to achieve an impressive accuracy of 99.68%. The findings demonstrate that deep learning can accurately

and rapidly categorize plant illnesses, improving agricultural efficiency.

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