Abstract:
In the agricultural landscape of Bangladesh, where farming plays a crucial role in the
country's economy, the well-being of plants is vital for ensuring food security. The process
of photosynthesis, occurring in the leaves, is essential for food production. However, the
occurrence of leaf diseases presents a considerable danger to food production, necessitating
the implementation of early detection measures. This study examines the transformative
domain of deep learning, particularly investigating the effectiveness of Convolutional
Neural Networks (CNNs) such as VGG19, DenseNet201, CNN, and InceptionV3 in
enhancing the preciseness of plant disease detection. In addition to highlighting the
inadequacy of these superficial learning models compared to traditional methods, our
research aims to acknowledge the constraints of manual observation and standard testing
by opposing the integration of state-of-the-art technologies in the agricultural sector. The
findings of this research extend beyond agriculture, offering potential solutions to
nutritional deficiencies and the possibility of increased crop yields. This paper not only
exhibits the potential of deep learning in revolutionizing plant disease recognition but also
provides a roadmap for future research. It emphasizes the crucial role that deep learning
plays in shaping sustainable farming practices and strengthening food production systems
against challenges, presenting a comprehensive plan for the intersection of technology and
agriculture in the pursuit of a resilient and well-nourished future.
