Abstract:
Purpose: This research aims to expedite the diagnosis of plant diseases to avert
agricultural losses. Considering that plant diseases account for up to 40% of global
food crop losses, this study employs advanced deep-learning techniques for rapid and
efficient diagnosis. This proactive strategy enhances agricultural output and
sustainability.
Design/methodology/approach: The study classified leaf images as healthy or
diseased using a CNN. Several pre-processing methods improve model correctness
and durability. Real agricultural photos were added to the training dataset to increase
the data collection. Hyperparameter adjustment and deep learning architecture
evaluation optimized model performance. The model was eventually incorporated into
a simple IoT smartphone app for real-time disease detection and reporting.
Findings: The research created an accurate CNN picture classification model. In
particular, the model obtained up to 95% accuracy on a smaller sample of 300
authentic leaf photos and 92% accuracy on an improved dataset of 2800 images. When
taught with additional data, the deep learning model may reliably identify plant
illnesses, making it a reliable early disease detection tool.
Practical implications: This model may be integrated into an IoT smartphone app to
help farmers and agricultural specialists monitor and manage diseases in real-time.
The method quickly and accurately identifies plant diseases, reducing crop losses,
improving food security, and benefiting farmers, particularly in agriculturally
dependent countries.
Originality value: Deep learning and IoT-based plant disease monitoring
technologies are novel in agricultural technology, and this study advances the field.
This method integrates accurate deep-learning models with real-world crop disease
control applications.
